PulmonDB - COVID19.
Regulons Exploration: cell lines
Monica Padilla
2023-01-25
Exploration of Regulons based on DA and RSS specificity
Preparation
Load libraries and regs-analysis.R script.
library(tidyverse) # data handling: dplyr, stringr
library(ComplexHeatmap) # graphics
library(circlize) # colors for complexheatmap
# library(RColorBrewer) # colors
library(org.Hs.eg.db) # orgDB hsapiens annotations
library(ggpubr) ## ggarrange
library(enrichplot) # graphics
library(clusterProfiler) # Biological Terms enrichment analysis + dot plots
library(pathview)
library(DOSE)
library(wordcloud)
library(RColorBrewer)
## Load functions
source("/home/user/Documents/mpadilla/COVID19/pyscenic/regulons-analysis/scripts/regs-analysis.R")Load general data and tidy:
## Load Data
# Load data: Experiment Metadata
meta <- as.data.frame(read_csv(params$metadata.fn, col_names = T))
if(params$celllines == 1){
meta <- meta %>% filter(disease == "Y") %>% dplyr::select(source, cellline) %>% distinct()
# format as in matrix for display
meta$cellline <- str_replace_all(meta$cellline, "-", "")
meta$cellline <- str_replace(meta$cellline, "A549_.+", "A549")
meta$source <- str_replace_all(meta$source, "-", "")
meta$source <- str_replace(meta$source, "w_vector_expressing_", "")
meta$source <- str_replace(meta$source, "1hr_Ruxolitinib_pretreatment", "pt")
} else {
meta <- meta %>% filter(disease == "Y") %>% select(source) %>% distinct()
}
# Load data: auc_mtx
auc_mtx <- as.data.frame(readRDS(paste0(params$inputdir,params$auc_mtx.fn)))
# Load data: TFs names (in order after just scenic counts cutoff)
tfs <- as.data.frame(read_csv(paste0(params$inputdir,params$tfs.fn), col_names = F))[,1]
tfs <- stringr::str_remove(tfs, "_\\(\\+\\)")
tfs <- tfs[match(str_remove(colnames(auc_mtx),"_\\(\\+\\)"),tfs)]
# Load data: tf targets (regulons)
tfs_targets <- readRDS(paste0(params$inputdir,params$tfs_targets.fn))
# Load data: RSS matrix
rss <- as.data.frame(read_csv(paste0(params$inputdir,params$RSS.fn), col_names = T))
rss <- column_to_rownames(rss, var="...1")
# rownames(rss) <- rownames(rss)[match(colnames(auc_mtx),rownames(rss))]
rownames(rss) <- rownames(rss)[match(str_replace(tfs,"$","_\\(\\+\\)"),rownames(rss))]
# Load and tidy data: log 2 FC (auc_mtx)
l2fc <- as.data.frame(read_csv(paste0(params$inputdir,params$l2fc.fn), col_names = T))
if(params$celllines==1){
names(l2fc) <- str_replace_all(names(l2fc), "-", "")
names(l2fc) <- str_replace(names(l2fc), "w_vector_expressing_", "")
names(l2fc) <- str_replace(names(l2fc), "1hr_Ruxolitinib_pretreatment", "pt")
}
# rownames(l2fc) <- colnames(auc_mtx)
rownames(l2fc) <- str_replace(tfs,"$","_\\(\\+\\)")
head(l2fc)# Load data: mwu adj pvalues
adj_pvals_mwu.fn <- paste0(params$inputdir,params$adj_pvals_klm.fn)
pvalues <- as.data.frame(read_csv(adj_pvals_mwu.fn, col_names = T))
rownames(pvalues) <- tfs
# selected regs - 2tailed tests + rss
tfs_da.fn <- paste0(params$inputdir,params$tfs_DA_2t.fn)
tfs_da <- readRDS(tfs_da.fn)
tfs_da_enrich.fn <- paste0(params$inputdir, params$tfs_DA_enrichLFC_2t.fn)
tfs_da_enrich <- readRDS(tfs_da_enrich.fn)
names(tfs_da_enrich) <- names(tfs_da)Define genes universe and graph number of genes per regulon:
# universe of genes = all genes from all tested regulons (after scenic iterations cutoff)
genes_universe <- c()
n.genes.per.reg <- c()
tested.regs <- names(tfs_targets) %in% str_replace(tfs, "$", "_\\(\\+\\)")
for (i in 1:length(names(tfs_targets))) {
if(tested.regs[i]){
genes_universe <- c(genes_universe,
str_remove(names(tfs_targets[i]), "_\\(\\+\\)"), # tf
unname(unlist(tfs_targets[i]))) # gene targets
n.genes.per.reg <- c(n.genes.per.reg, length(unname(unlist(tfs_targets[i]))))
}
}
genes_universe.df <- bitr(unique(genes_universe), fromType = "SYMBOL",
toType = c("ENSEMBL", "ENTREZID"),
OrgDb = org.Hs.eg.db)
genes_universe <- genes_universe.df$ENTREZID # subset to genes found in OrgDb
#genes_universe <- symbol2EntrezID(genenames = unique(genes_universe))
names(n.genes.per.reg) <- tfs
summary(n.genes.per.reg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.0 171.2 441.5 1079.5 1338.2 7747.0# graph # target genes per regulon histogram
n.genes.per.reg <- as.data.frame(n.genes.per.reg)
p <- ggplot(n.genes.per.reg,aes(n.genes.per.reg)) + #<<
geom_bar(fill="#8274c9") + # geom hist didnt work
scale_x_binned() +
labs(x="# target genes per regulon", y="") +
geom_vline(xintercept = mean(n.genes.per.reg$n.genes.per.reg), color = "red") +
geom_vline(xintercept = median(n.genes.per.reg$n.genes.per.reg), color = "blue")
p + annotate("text", x = 3500, y = 450,
label = c(summary(n.genes.per.reg)[3,])) +
annotate("text", x = 3500, y = 550,
label = c(summary(n.genes.per.reg)[4,]))
## save to png
# png(paste0(params$outdir,"hist_genesxregulon.png"), width = 600, height = 200)
# p + annotate("text", x = 3500, y = 450,
# label = c(summary(n.genes.per.reg)[3,])) +
# annotate("text", x = 3500, y = 550,
# label = c(summary(n.genes.per.reg)[4,]))
# dev.off()Make lists of regulons defined as upregulated or downregulated:
## Make lists of up/down regulons
tfs_da_up = list()
tfs_da_down = list()
tfs_da_enrich_up = list()
tfs_da_enrich_down = list()
for (exp in names(tfs_da)) {
tfs_da_up[[exp]] <- tfs_da[[exp]][tfs_da[[exp]] %in% rownames(l2fc)[which(l2fc[exp] > 0)]]
tfs_da_down[[exp]] <- tfs_da[[exp]][tfs_da[[exp]] %in% rownames(l2fc)[which(l2fc[exp] < 0)]]
tfs_da_enrich_up[[exp]] <- intersect(tfs_da_enrich[[exp]], tfs_da_up[[exp]])
tfs_da_enrich_down[[exp]] <- intersect(tfs_da_enrich[[exp]], tfs_da_down[[exp]])
}Plots: DA
Upset Plot
library(tidyverse)
library(ComplexHeatmap)
library(RColorBrewer)
# tfs_da
## Check intersections in pairs of experiments
#has.intersections <- checkCommonRegs(tfs_per_exp_list = tfs_da_up)
#common.regs.up <- checkCommonRegsTable(tfs_per_exp_list = tfs_da_up)
## Upset Plot
# subset for experiments that had intersections with other experiments, also plot only intersections >=2
# m <- make_comb_mat(tfs_da_up[names(tfs_da_up) %in% has.intersections],
# mode = "distinct")
# m <- m[comb_degree(m) >= 2]
#
Upregulated Regulons
Upset Plot of distinctly shared upregulated regulons between experiments:
m <- make_comb_mat(tfs_da_up, mode = "distinct")
# m <- m[6:10,]
ss = set_size(m)
cs = comb_size(m)
ht = UpSet(m,
set_order = order(ss),
comb_order = order(comb_degree(m), -cs),
top_annotation = HeatmapAnnotation(
"Regulons Intersection" = anno_barplot(cs,
ylim = c(0, max(cs)*1.1),
border = FALSE,
gp = gpar(fill = "black"),
height = unit(4, "cm")
),
annotation_name_side = "left",
annotation_name_rot = 90),
left_annotation = rowAnnotation(
"Regulons Per Experiment" = anno_barplot(-ss,
baseline = 0,
axis_param = list(
at = c(0, -500, -1000, -1500),
labels = c(0, 500, 1000, 1500),
labels_rot = 0),
border = FALSE,
gp = gpar(fill = "black"),
width = unit(4, "cm")
),
set_name = anno_text(set_name(m),
location = 0.5,
just = "center",
width = max_text_width(set_name(m)) + unit(1, "mm"))
),
right_annotation = NULL,
show_row_names = FALSE,
pt_size = unit(3, "mm"),
lwd = 2,
comb_col = RColorBrewer::brewer.pal(n = max(comb_degree(m)), name = "Set1")[comb_degree(m)]
)
ht = draw(ht)
od = column_order(ht)
decorate_annotation("Regulons Intersection", {
grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
default.units = "native", just = c("left", "bottom"),
gp = gpar(fontsize = 9, col = "#404040"), rot = 45)
})
# ## save to png
# png(paste0(params$outdir,"upset_upregulated.png"), width = 1700, height = 600)
# ht = draw(ht)
# od = column_order(ht)
# decorate_annotation("Regulons Intersection", {
# grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
# default.units = "native", just = c("left", "bottom"),
# gp = gpar(fontsize = 9, col = "#404040"), rot = 45)
# })
# dev.off()Pairwise comparison of shared regulons, distinctly shared regulons are shown in the next table:
Sets were compared twice, once showing which are unique to each experiment, and the second time excluding comparison of a experiment to itself so sharing of regulons between regulons could be found.
tableCommonDistinct <- distinctlySharedRegs(tfs_per_exp_list = tfs_da_up)
tableCommonDistinct# save table
# write_csv(tableCommonDistinct,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_up.csv"), col_names = T)
## subset for SARS-CoV-2 infected experiments
tableup.sc2 <- tableCommonDistinct %>% dplyr::select(6:10) %>% dplyr::slice(6:10)
View(tableup.sc2)
# save table
# write_csv(tableup.sc2,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_up_subSC2.csv"), col_names = T)Regulons that are only present in SARS-CoV-2 infection samples:
getDistinct(tfs_per_exp_list = tfs_da_up, indexInterestGroup = 6:10)## [1] "AHCTF1" "AIRE" "ALX1" "ARID5B" "ARNT" "ATF1" "BCL3"
## [8] "CEBPB" "CEBPG" "CREB1" "CREB3" "CREBL2" "DLX4" "DMRTA1"
## [15] "DUXA" "EBF1" "EGR2" "ERF" "ETV1" "FOSL1" "FOXA2"
## [22] "FOXF2" "FOXK2" "FOXP3" "FOXQ1" "GATA3" "HDAC2" "HLF"
## [29] "HNF1A" "HNF1B" "HOXA2" "HOXA5" "HOXA7" "HOXA9" "HOXB5"
## [36] "HOXB6" "HOXB7" "HOXB8" "HOXB9" "HOXC10" "HOXC4" "HOXC5"
## [43] "HOXC6" "HOXC8" "ING3" "IRF4" "IRF5" "ISL2" "JUN"
## [50] "KLF10" "LHX1" "LHX4" "LHX8" "LHX9" "MAFF" "MEOX2"
## [57] "MIOS" "MSX1" "MSX2" "MYEF2" "NFYB" "NHLH1" "NR1I2"
## [64] "NR4A1" "NRF1" "ONECUT2" "PAX2" "PAX7" "PBX1" "PDX1"
## [71] "POU2F3" "RBBP5" "RFX5" "SMC3" "SOX2" "SP1" "SP2"
## [78] "SP4" "SP7" "STAT1" "TAF7" "TAL1" "TBP" "TBPL1"
## [85] "TBPL2" "TBX15" "TBX3" "TET1" "TFE3" "UBTF" "UQCRB"
## [92] "VSX1" "WT1" "ZEB1" "ZFHX2" "ZFY" "ZNF160" "ZNF502"
## [99] "ZNF580"
Downregulated Regulons
Upset Plot of distinctly shared downregulated regulons between experiments:
m <- make_comb_mat(tfs_da_down, mode = "distinct")
# m <- m[6:10,]
ss = set_size(m)
cs = comb_size(m)
ht = UpSet(m,
set_order = order(ss),
comb_order = order(comb_degree(m), -cs),
top_annotation = HeatmapAnnotation(
"Regulons Intersection" = anno_barplot(cs,
ylim = c(0, max(cs)*1.1),
border = FALSE,
gp = gpar(fill = "black"),
height = unit(4, "cm")
),
annotation_name_side = "left",
annotation_name_rot = 90),
left_annotation = rowAnnotation(
"Regulons Per Experiment" = anno_barplot(-ss,
baseline = 0,
axis_param = list(
at = c(0, -500, -1000, -1500),
labels = c(0, 500, 1000, 1500),
labels_rot = 0),
border = FALSE,
gp = gpar(fill = "black"),
width = unit(4, "cm")
),
set_name = anno_text(set_name(m),
location = 0.5,
just = "center",
width = max_text_width(set_name(m)) + unit(1, "mm"))
),
right_annotation = NULL,
show_row_names = FALSE,
pt_size = unit(3, "mm"),
lwd = 2,
comb_col = RColorBrewer::brewer.pal(n = max(comb_degree(m)), name = "Set1")[comb_degree(m)]
)
ht = draw(ht)
od = column_order(ht)
decorate_annotation("Regulons Intersection", {
grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
default.units = "native", just = c("left", "bottom"),
gp = gpar(fontsize = 9, col = "#404040"), rot = 45)
})
# ## save to png
# png(paste0(params$outdir,"upset_downregulated.png"), width = 1200, height = 600)
# ht = draw(ht)
# od = column_order(ht)
# decorate_annotation("Regulons Intersection", {
# grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
# default.units = "native", just = c("left", "bottom"),
# gp = gpar(fontsize = 9, col = "#404040"), rot = 45)
# })
# dev.off()Pairwise comparison of shared regulons, distinctly shared downregulated regulons are shown in the next table:
tabledown <- distinctlySharedRegs(tfs_per_exp_list = tfs_da_down)
tabledown# save table
# write_csv(tabledown,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_down.csv"), col_names = T)
## subset for SARS-CoV-2 infected experiments
tabledown.sc2 <- tabledown %>%
dplyr::select(6:10) %>%
dplyr::slice(6:10)
View(tabledown.sc2)
# save table
# write_csv(tabledown.sc2,
# file = paste0(params$outdir,
# "tableDistinctlySharedRegulons_down_subSC2.csv"),
# col_names = T)Regulons that were only turned off in SARS-CoV-2 infection samples:
getDistinct(tfs_per_exp_list = tfs_da_down, indexInterestGroup = 6:10)## [1] "BATF" "BBX" "BCL6B" "CKMT1B" "DMRT1" "ESRRA" "HES5" "HSF2"
## [9] "IRF3" "IRX2" "KLF5" "MLXIP" "MXD4" "NKX2-1" "OVOL2" "PKNOX1"
## [17] "RARG" "SIX2" "SOX13" "SREBF2" "TAGLN2" "TCF7" "TFAP2A" "TLX3"
## [25] "TWIST1" "ZNF471" "ZNF560" "ZNF768" "ZNF773" "ZXDC"Heatmap
## Load and tidy data to plot: pvalues adjusted of regulons in DA [UP|DOWN]
# Load and tidy data: regulons/tfs that were in DA + enriched
paste0("using set of DA regulons (tfs): ",tfs_da.fn)## [1] "using set of DA regulons (tfs): ../regulons-analysis/out/celllines/tfs_mwu_ks_fdr1-24-2t.RData"tfs_da <- readRDS(tfs_da.fn)
if(params$celllines==1){
names(tfs_da) <- str_replace_all(names(tfs_da), "-", "")
names(tfs_da) <- str_replace(names(tfs_da), "_w_vector_expressing", "")
names(tfs_da) <- str_replace(names(tfs_da), "1hr_Ruxolitinib_pretreatment", "pt")
}
tfs_da## $HPIV3_infected_A549
## [1] "ALX3_(+)" "ALX4_(+)" "ARNT_(+)" "ARNTL_(+)" "ATF1_(+)"
## [6] "ATF4_(+)" "ATF5_(+)" "BCL6B_(+)" "BHLHE40_(+)" "BHLHE41_(+)"
## [11] "BRF1_(+)" "CDX1_(+)" "CHD2_(+)" "CLOCK_(+)" "CPEB1_(+)"
## [16] "CREB1_(+)" "CREB5_(+)" "DDIT3_(+)" "DLX2_(+)" "E2F1_(+)"
## [21] "E4F1_(+)" "ELF2_(+)" "ESRRA_(+)" "FOSL1_(+)" "FOXA2_(+)"
## [26] "FOXB1_(+)" "FOXC2_(+)" "FOXF2_(+)" "FOXK1_(+)" "FOXO1_(+)"
## [31] "FOXO3_(+)" "FOXP2_(+)" "GABPB1_(+)" "GCM1_(+)" "HAND1_(+)"
## [36] "HES6_(+)" "HEY1_(+)" "HEY2_(+)" "HIVEP2_(+)" "HLF_(+)"
## [41] "HOXA9_(+)" "HOXB2_(+)" "HOXB6_(+)" "HOXB8_(+)" "HOXB9_(+)"
## [46] "HOXC10_(+)" "HOXC13_(+)" "HOXC8_(+)" "HOXD11_(+)" "HSF2_(+)"
## [51] "IKZF2_(+)" "IRF4_(+)" "ISL2_(+)" "KDM5A_(+)" "KLF3_(+)"
## [56] "KLF7_(+)" "KLF9_(+)" "LHX4_(+)" "MAF_(+)" "MAZ_(+)"
## [61] "MECOM_(+)" "MXD3_(+)" "MXI1_(+)" "MYCN_(+)" "MYEF2_(+)"
## [66] "NEUROD1_(+)" "NFE2L1_(+)" "NFIA_(+)" "NFIL3_(+)" "NFKB1_(+)"
## [71] "NFKB2_(+)" "NFYA_(+)" "NFYC_(+)" "NHLH1_(+)" "NKX3-1_(+)"
## [76] "NR2C1_(+)" "NR5A2_(+)" "OTP_(+)" "PAX7_(+)" "POU3F2_(+)"
## [81] "POU5F1_(+)" "RARB_(+)" "RFX2_(+)" "RFX3_(+)" "RREB1_(+)"
## [86] "RUNX2_(+)" "SNAI3_(+)" "SOX1_(+)" "SOX15_(+)" "SP6_(+)"
## [91] "TAF7_(+)" "TAGLN2_(+)" "TAL1_(+)" "TBX15_(+)" "TBX20_(+)"
## [96] "TCF3_(+)" "TFE3_(+)" "TFEB_(+)" "THAP1_(+)" "VSX1_(+)"
## [101] "ZBTB33_(+)" "ZBTB7A_(+)" "ZEB1_(+)" "ZFX_(+)" "ZIC2_(+)"
## [106] "ZNF134_(+)" "ZNF143_(+)" "ZNF148_(+)" "ZNF160_(+)" "ZNF227_(+)"
## [111] "ZNF354C_(+)" "ZNF429_(+)" "ZNF449_(+)" "ZNF502_(+)" "ZNF524_(+)"
## [116] "ZNF571_(+)" "ZNF597_(+)" "ZNF607_(+)" "ZNF668_(+)" "ZNF672_(+)"
## [121] "ZNF787_(+)" "ZNF841_(+)" "ZSCAN26_(+)"
##
## $IAV_infected_A549
## [1] "ALX1_(+)" "ALX4_(+)" "ARNT_(+)" "ARNT2_(+)" "ARNTL2_(+)"
## [6] "ASCL2_(+)" "ATF2_(+)" "ATF4_(+)" "BACH1_(+)" "BATF_(+)"
## [11] "BBX_(+)" "BCL11A_(+)" "BCL11B_(+)" "BCLAF1_(+)" "BHLHE40_(+)"
## [16] "BRF1_(+)" "CDX2_(+)" "CLOCK_(+)" "CPEB1_(+)" "CREB5_(+)"
## [21] "CTCFL_(+)" "DLX3_(+)" "DLX5_(+)" "E2F1_(+)" "E2F2_(+)"
## [26] "E2F4_(+)" "E2F5_(+)" "E2F6_(+)" "E2F8_(+)" "EGR1_(+)"
## [31] "EGR3_(+)" "EHF_(+)" "ELF1_(+)" "ELF4_(+)" "ELF5_(+)"
## [36] "ELK3_(+)" "ELK4_(+)" "EN1_(+)" "EN2_(+)" "ERF_(+)"
## [41] "ESRRA_(+)" "ETS1_(+)" "ETS2_(+)" "ETV1_(+)" "ETV7_(+)"
## [46] "FOSB_(+)" "FOSL2_(+)" "FOXB1_(+)" "FOXC1_(+)" "FOXD3_(+)"
## [51] "FOXF2_(+)" "FOXM1_(+)" "FOXO1_(+)" "GABPA_(+)" "GABPB1_(+)"
## [56] "GATA5_(+)" "GCM1_(+)" "GLI1_(+)" "GLIS2_(+)" "GRHL1_(+)"
## [61] "GRHL3_(+)" "GRHPR_(+)" "GSC_(+)" "GSX2_(+)" "GTF2B_(+)"
## [66] "GTF2IRD1_(+)" "HES1_(+)" "HES5_(+)" "HES6_(+)" "HIC1_(+)"
## [71] "HINFP_(+)" "HIVEP2_(+)" "HLX_(+)" "HNF1A_(+)" "HNF1B_(+)"
## [76] "HOXA1_(+)" "HOXA2_(+)" "HOXA5_(+)" "HOXA6_(+)" "HOXA7_(+)"
## [81] "HOXA9_(+)" "HOXB13_(+)" "HOXB5_(+)" "HOXB7_(+)" "HOXB8_(+)"
## [86] "HOXC10_(+)" "HOXC11_(+)" "HOXC13_(+)" "HOXC5_(+)" "HOXC6_(+)"
## [91] "HOXD3_(+)" "HOXD4_(+)" "HOXD9_(+)" "HTATIP2_(+)" "IRF1_(+)"
## [96] "IRF2_(+)" "IRF3_(+)" "IRF5_(+)" "IRF6_(+)" "IRF7_(+)"
## [101] "IRF9_(+)" "IRX1_(+)" "IRX2_(+)" "IRX4_(+)" "IRX5_(+)"
## [106] "JUND_(+)" "KLF10_(+)" "KLF3_(+)" "KLF4_(+)" "KLF5_(+)"
## [111] "KLF6_(+)" "KLF8_(+)" "KLF9_(+)" "LBX2_(+)" "LHX2_(+)"
## [116] "LHX5_(+)" "LMX1B_(+)" "MAFA_(+)" "MAFB_(+)" "MAFG_(+)"
## [121] "MAX_(+)" "MAZ_(+)" "MECOM_(+)" "MEOX1_(+)" "MNX1_(+)"
## [126] "MSX2_(+)" "MTF1_(+)" "MXD1_(+)" "MXD3_(+)" "MXD4_(+)"
## [131] "MXI1_(+)" "MYCN_(+)" "MYEF2_(+)" "MYPOP_(+)" "MZF1_(+)"
## [136] "NEUROD1_(+)" "NFATC4_(+)" "NFE2_(+)" "NFE2L1_(+)" "NFIA_(+)"
## [141] "NFKB1_(+)" "NFYA_(+)" "NFYC_(+)" "NHLH2_(+)" "NKX2-1_(+)"
## [146] "NKX3-1_(+)" "NR1H3_(+)" "NR1H4_(+)" "NR2E1_(+)" "NR2F1_(+)"
## [151] "NR2F6_(+)" "ONECUT2_(+)" "OTP_(+)" "OTX1_(+)" "OVOL2_(+)"
## [156] "PAX7_(+)" "PAX9_(+)" "PHF21A_(+)" "PHF8_(+)" "PKNOX1_(+)"
## [161] "POLE4_(+)" "POLR2A_(+)" "POLR3A_(+)" "POLR3G_(+)" "POU2F1_(+)"
## [166] "POU2F3_(+)" "POU3F1_(+)" "POU3F4_(+)" "PPARD_(+)" "PRDM1_(+)"
## [171] "PRDM15_(+)" "PRRX2_(+)" "PSMD12_(+)" "RAB18_(+)" "RARG_(+)"
## [176] "REL_(+)" "RFX2_(+)" "RORA_(+)" "RREB1_(+)" "RXRG_(+)"
## [181] "SATB1_(+)" "SETDB1_(+)" "SIN3A_(+)" "SIX2_(+)" "SIX5_(+)"
## [186] "SMAD1_(+)" "SMARCA5_(+)" "SMARCC2_(+)" "SNAI2_(+)" "SNAI3_(+)"
## [191] "SOX5_(+)" "SP3_(+)" "SP5_(+)" "STAT1_(+)" "STAT2_(+)"
## [196] "STAT5A_(+)" "STAT5B_(+)" "TAGLN2_(+)" "TBPL2_(+)" "TCF12_(+)"
## [201] "TCF3_(+)" "TCF7_(+)" "TEAD4_(+)" "TEF_(+)" "TFAP2A_(+)"
## [206] "TFAP2C_(+)" "TFAP2E_(+)" "TFDP2_(+)" "TGIF2_(+)" "TLX2_(+)"
## [211] "TRIM28_(+)" "USF1_(+)" "USF2_(+)" "VAX1_(+)" "VDR_(+)"
## [216] "VSX2_(+)" "ZBTB7A_(+)" "ZFX_(+)" "ZNF134_(+)" "ZNF143_(+)"
## [221] "ZNF208_(+)" "ZNF219_(+)" "ZNF274_(+)" "ZNF281_(+)" "ZNF35_(+)"
## [226] "ZNF354C_(+)" "ZNF426_(+)" "ZNF429_(+)" "ZNF471_(+)" "ZNF483_(+)"
## [231] "ZNF502_(+)" "ZNF524_(+)" "ZNF554_(+)" "ZNF560_(+)" "ZNF597_(+)"
## [236] "ZNF672_(+)" "ZNF682_(+)" "ZNF720_(+)" "ZNF729_(+)" "ZNF730_(+)"
## [241] "ZNF761_(+)" "ZNF773_(+)" "ZNF787_(+)"
##
## $IAV_infected_NHBE
## [1] "AHCTF1_(+)" "AIRE_(+)" "ALX1_(+)"
## [4] "ALX3_(+)" "ALX4_(+)" "AR_(+)"
## [7] "ARID5B_(+)" "ARNT_(+)" "ARNTL2_(+)"
## [10] "ATF1_(+)" "ATF3_(+)" "BACH2_(+)"
## [13] "BATF_(+)" "BCL3_(+)" "BCLAF1_(+)"
## [16] "BDP1_(+)" "BHLHE41_(+)" "BNC2_(+)"
## [19] "BORCS8-MEF2B_(+)" "BRF1_(+)" "CCNT2_(+)"
## [22] "CDX2_(+)" "CEBPA_(+)" "CEBPB_(+)"
## [25] "CEBPD_(+)" "CELF6_(+)" "CHD2_(+)"
## [28] "CKMT1B_(+)" "CLOCK_(+)" "CNOT3_(+)"
## [31] "CPEB1_(+)" "CPSF4_(+)" "CREB1_(+)"
## [34] "CREB3_(+)" "CREB3L1_(+)" "CUX1_(+)"
## [37] "DBX1_(+)" "DLX2_(+)" "DLX4_(+)"
## [40] "DMRT1_(+)" "DMRTA1_(+)" "DUXA_(+)"
## [43] "E2F3_(+)" "E2F4_(+)" "E2F6_(+)"
## [46] "EGR1_(+)" "EGR2_(+)" "EGR3_(+)"
## [49] "ELK3_(+)" "EMX1_(+)" "EN1_(+)"
## [52] "EN2_(+)" "ENO1_(+)" "EP300_(+)"
## [55] "ERF_(+)" "ESRRA_(+)" "ESRRB_(+)"
## [58] "ETV1_(+)" "ETV3_(+)" "ETV5_(+)"
## [61] "EVX2_(+)" "FGF19_(+)" "FOS_(+)"
## [64] "FOSL1_(+)" "FOXA1_(+)" "FOXA2_(+)"
## [67] "FOXB1_(+)" "FOXC1_(+)" "FOXC2_(+)"
## [70] "FOXD2_(+)" "FOXD3_(+)" "FOXD4_(+)"
## [73] "FOXD4L6_(+)" "FOXF2_(+)" "FOXJ2_(+)"
## [76] "FOXK1_(+)" "FOXK2_(+)" "FOXM1_(+)"
## [79] "FOXN1_(+)" "FOXO1_(+)" "FOXO3_(+)"
## [82] "FOXO6_(+)" "FOXQ1_(+)" "GATA3_(+)"
## [85] "GCM1_(+)" "GLIS2_(+)" "GMEB1_(+)"
## [88] "GRHL2_(+)" "GTF2IRD1_(+)" "HAND1_(+)"
## [91] "HDAC1_(+)" "HES5_(+)" "HEY1_(+)"
## [94] "HINFP_(+)" "HLF_(+)" "HMGB1_(+)"
## [97] "HNF1A_(+)" "HNF1B_(+)" "HOXA10_(+)"
## [100] "HOXA2_(+)" "HOXA5_(+)" "HOXA9_(+)"
## [103] "HOXB13_(+)" "HOXB4_(+)" "HOXB6_(+)"
## [106] "HOXB8_(+)" "HOXB9_(+)" "HOXC10_(+)"
## [109] "HOXC13_(+)" "HOXC4_(+)" "HOXC6_(+)"
## [112] "HOXC8_(+)" "HOXC9_(+)" "HOXD11_(+)"
## [115] "HOXD13_(+)" "HOXD3_(+)" "HOXD4_(+)"
## [118] "HSF2_(+)" "HSF4_(+)" "HTATIP2_(+)"
## [121] "IKZF3_(+)" "ING3_(+)" "IRF1_(+)"
## [124] "IRF4_(+)" "IRX2_(+)" "IRX5_(+)"
## [127] "ISL2_(+)" "JUN_(+)" "JUNB_(+)"
## [130] "JUND_(+)" "KDM5A_(+)" "KLF1_(+)"
## [133] "KLF10_(+)" "KLF11_(+)" "KLF12_(+)"
## [136] "KLF13_(+)" "KLF14_(+)" "KLF2_(+)"
## [139] "KLF5_(+)" "KLF8_(+)" "LHX4_(+)"
## [142] "LHX8_(+)" "LHX9_(+)" "LMX1B_(+)"
## [145] "MAF_(+)" "MAFB_(+)" "MAFF_(+)"
## [148] "MAFG_(+)" "MEF2B_(+)" "MEF2C_(+)"
## [151] "MEOX2_(+)" "MLXIP_(+)" "MLXIPL_(+)"
## [154] "MNT_(+)" "MNX1_(+)" "MSX1_(+)"
## [157] "MSX2_(+)" "MYEF2_(+)" "MYOD1_(+)"
## [160] "MZF1_(+)" "NFATC2_(+)" "NFE2L2_(+)"
## [163] "NFE2L3_(+)" "NFIA_(+)" "NFIB_(+)"
## [166] "NFIC_(+)" "NFIL3_(+)" "NFKB2_(+)"
## [169] "NHLH1_(+)" "NKX2-1_(+)" "NKX2-5_(+)"
## [172] "NKX3-1_(+)" "NPDC1_(+)" "NR0B1_(+)"
## [175] "NR1D1_(+)" "NR2E1_(+)" "NR2F1_(+)"
## [178] "NR3C1_(+)" "NR4A1_(+)" "NR5A2_(+)"
## [181] "NR6A1_(+)" "NRF1_(+)" "ONECUT1_(+)"
## [184] "ONECUT2_(+)" "ONECUT3_(+)" "OTP_(+)"
## [187] "PATZ1_(+)" "PAX7_(+)" "PDX1_(+)"
## [190] "PKNOX1_(+)" "POLE3_(+)" "POLR2A_(+)"
## [193] "POLR3G_(+)" "POU3F2_(+)" "POU3F4_(+)"
## [196] "POU5F1_(+)" "PPARD_(+)" "PROX1_(+)"
## [199] "PRRX1_(+)" "PSMD12_(+)" "RARB_(+)"
## [202] "RARG_(+)" "RBBP5_(+)" "REL_(+)"
## [205] "RELA_(+)" "RFX1_(+)" "RFX2_(+)"
## [208] "RFX3_(+)" "RFX5_(+)" "RFXAP_(+)"
## [211] "SATB1_(+)" "SIN3A_(+)" "SIX2_(+)"
## [214] "SIX4_(+)" "SIX5_(+)" "SOHLH2_(+)"
## [217] "SOX1_(+)" "SOX13_(+)" "SOX15_(+)"
## [220] "SOX2_(+)" "SOX21_(+)" "SOX4_(+)"
## [223] "SOX6_(+)" "SP7_(+)" "SP9_(+)"
## [226] "SPDEF_(+)" "SREBF2_(+)" "STAT4_(+)"
## [229] "TAF7_(+)" "TAGLN2_(+)" "TAL1_(+)"
## [232] "TBP_(+)" "TBPL1_(+)" "TBX15_(+)"
## [235] "TBX20_(+)" "TBX3_(+)" "TBX6_(+)"
## [238] "TCF4_(+)" "TCF7_(+)" "TET1_(+)"
## [241] "TFAP2A_(+)" "TFDP1_(+)" "TFE3_(+)"
## [244] "TFEB_(+)" "THAP1_(+)" "TLX2_(+)"
## [247] "TLX3_(+)" "TRPS1_(+)" "UQCRB_(+)"
## [250] "USF2_(+)" "VEZF1_(+)" "VSX1_(+)"
## [253] "VSX2_(+)" "XBP1_(+)" "YBX1_(+)"
## [256] "ZBTB41_(+)" "ZBTB43_(+)" "ZEB1_(+)"
## [259] "ZFHX2_(+)" "ZIC2_(+)" "ZNF160_(+)"
## [262] "ZNF189_(+)" "ZNF208_(+)" "ZNF239_(+)"
## [265] "ZNF263_(+)" "ZNF264_(+)" "ZNF274_(+)"
## [268] "ZNF281_(+)" "ZNF316_(+)" "ZNF441_(+)"
## [271] "ZNF442_(+)" "ZNF467_(+)" "ZNF471_(+)"
## [274] "ZNF486_(+)" "ZNF502_(+)" "ZNF513_(+)"
## [277] "ZNF560_(+)" "ZNF571_(+)" "ZNF597_(+)"
## [280] "ZNF668_(+)" "ZNF713_(+)" "ZNF721_(+)"
## [283] "ZNF740_(+)" "ZNF816_(+)" "ZNF836_(+)"
## [286] "ZSCAN31_(+)" "ZXDC_(+)"
##
## $IAVdNS1_infected_NHBE
## [1] "AIRE_(+)" "ALX1_(+)" "AR_(+)" "ARNT_(+)" "ARNTL2_(+)"
## [6] "ATF1_(+)" "ATF3_(+)" "ATF5_(+)" "BACH2_(+)" "BATF_(+)"
## [11] "BCL3_(+)" "BCLAF1_(+)" "BDP1_(+)" "BHLHE40_(+)" "BHLHE41_(+)"
## [16] "BNC2_(+)" "BRF1_(+)" "CCNT2_(+)" "CDX2_(+)" "CEBPA_(+)"
## [21] "CEBPB_(+)" "CEBPD_(+)" "CELF6_(+)" "CHD2_(+)" "CKMT1B_(+)"
## [26] "CNOT3_(+)" "CPSF4_(+)" "CREB1_(+)" "CREB3_(+)" "CREB3L1_(+)"
## [31] "CTCF_(+)" "DBX1_(+)" "DDIT3_(+)" "DLX2_(+)" "E2F3_(+)"
## [36] "E2F4_(+)" "E2F6_(+)" "E2F8_(+)" "EGR1_(+)" "EGR2_(+)"
## [41] "EGR3_(+)" "ELK3_(+)" "EMX1_(+)" "EN2_(+)" "ENO1_(+)"
## [46] "EP300_(+)" "ERF_(+)" "ESR2_(+)" "ESRRA_(+)" "ESRRB_(+)"
## [51] "ETV1_(+)" "ETV3_(+)" "ETV5_(+)" "EVX2_(+)" "FGF19_(+)"
## [56] "FOS_(+)" "FOSL1_(+)" "FOXA1_(+)" "FOXA2_(+)" "FOXB1_(+)"
## [61] "FOXC1_(+)" "FOXC2_(+)" "FOXD2_(+)" "FOXD3_(+)" "FOXD4_(+)"
## [66] "FOXD4L4_(+)" "FOXD4L6_(+)" "FOXK1_(+)" "FOXK2_(+)" "FOXM1_(+)"
## [71] "FOXO1_(+)" "FOXO3_(+)" "FOXO6_(+)" "FOXQ1_(+)" "GATA3_(+)"
## [76] "GATA6_(+)" "GCM1_(+)" "GMEB1_(+)" "GMEB2_(+)" "GPD1_(+)"
## [81] "GRHL2_(+)" "GSX1_(+)" "GTF2IRD1_(+)" "HAND1_(+)" "HDAC1_(+)"
## [86] "HES5_(+)" "HIC1_(+)" "HINFP_(+)" "HLF_(+)" "HMGB1_(+)"
## [91] "HNF1B_(+)" "HOXA10_(+)" "HOXA2_(+)" "HOXA5_(+)" "HOXA9_(+)"
## [96] "HOXB13_(+)" "HOXB2_(+)" "HOXB6_(+)" "HOXB8_(+)" "HOXB9_(+)"
## [101] "HOXC10_(+)" "HOXC13_(+)" "HOXC4_(+)" "HOXC6_(+)" "HOXC8_(+)"
## [106] "HOXC9_(+)" "HOXD11_(+)" "HOXD13_(+)" "HOXD3_(+)" "HOXD4_(+)"
## [111] "HSF2_(+)" "HSF4_(+)" "HTATIP2_(+)" "IKZF3_(+)" "ING3_(+)"
## [116] "IRF1_(+)" "IRF4_(+)" "IRX2_(+)" "IRX5_(+)" "ISL2_(+)"
## [121] "JUN_(+)" "JUNB_(+)" "JUND_(+)" "KDM5A_(+)" "KLF1_(+)"
## [126] "KLF11_(+)" "KLF12_(+)" "KLF14_(+)" "KLF2_(+)" "KLF5_(+)"
## [131] "KLF8_(+)" "LHX4_(+)" "LHX8_(+)" "LHX9_(+)" "MAF_(+)"
## [136] "MAFB_(+)" "MAFF_(+)" "MAFG_(+)" "MEF2B_(+)" "MLXIP_(+)"
## [141] "MLXIPL_(+)" "MNT_(+)" "MNX1_(+)" "MSX1_(+)" "MSX2_(+)"
## [146] "MXD4_(+)" "MXI1_(+)" "MYEF2_(+)" "MYOD1_(+)" "MYPOP_(+)"
## [151] "MZF1_(+)" "NFATC2_(+)" "NFE2L1_(+)" "NFE2L2_(+)" "NFIA_(+)"
## [156] "NFIB_(+)" "NFIC_(+)" "NFIL3_(+)" "NFKB2_(+)" "NHLH1_(+)"
## [161] "NHLH2_(+)" "NKX2-1_(+)" "NKX3-1_(+)" "NPDC1_(+)" "NR0B1_(+)"
## [166] "NR1D1_(+)" "NR2C1_(+)" "NR2F1_(+)" "NR4A1_(+)" "NR5A2_(+)"
## [171] "NR6A1_(+)" "NRF1_(+)" "ONECUT1_(+)" "ONECUT2_(+)" "ONECUT3_(+)"
## [176] "OTP_(+)" "PATZ1_(+)" "PAX7_(+)" "PHF21A_(+)" "PKNOX1_(+)"
## [181] "POLE3_(+)" "POLR2A_(+)" "POLR3A_(+)" "POLR3G_(+)" "POU1F1_(+)"
## [186] "POU3F2_(+)" "POU3F4_(+)" "POU4F3_(+)" "POU5F1_(+)" "PPARD_(+)"
## [191] "PROX1_(+)" "PSMD12_(+)" "RARB_(+)" "RARG_(+)" "RBBP5_(+)"
## [196] "REL_(+)" "RELA_(+)" "RFX1_(+)" "RFX2_(+)" "RFX3_(+)"
## [201] "RFX5_(+)" "RFXAP_(+)" "SHOX2_(+)" "SIX4_(+)" "SIX5_(+)"
## [206] "SMAD3_(+)" "SNAPC4_(+)" "SOHLH2_(+)" "SOX1_(+)" "SOX13_(+)"
## [211] "SOX15_(+)" "SOX21_(+)" "SOX4_(+)" "SOX6_(+)" "SP9_(+)"
## [216] "SPDEF_(+)" "SREBF2_(+)" "STAT4_(+)" "TAF7_(+)" "TAGLN2_(+)"
## [221] "TAL1_(+)" "TBP_(+)" "TBPL1_(+)" "TBX18_(+)" "TBX20_(+)"
## [226] "TBX3_(+)" "TCF4_(+)" "TCF7_(+)" "TEF_(+)" "TET1_(+)"
## [231] "TFAP2A_(+)" "TFDP1_(+)" "TFE3_(+)" "TFEB_(+)" "THAP1_(+)"
## [236] "TLX3_(+)" "TRPS1_(+)" "UBP1_(+)" "UBTF_(+)" "UQCRB_(+)"
## [241] "VEZF1_(+)" "VSX1_(+)" "VSX2_(+)" "XBP1_(+)" "YBX1_(+)"
## [246] "YY2_(+)" "ZBTB41_(+)" "ZBTB43_(+)" "ZDHHC15_(+)" "ZEB1_(+)"
## [251] "ZFHX2_(+)" "ZFHX3_(+)" "ZFP64_(+)" "ZFX_(+)" "ZIC2_(+)"
## [256] "ZNF143_(+)" "ZNF160_(+)" "ZNF205_(+)" "ZNF208_(+)" "ZNF239_(+)"
## [261] "ZNF25_(+)" "ZNF263_(+)" "ZNF264_(+)" "ZNF274_(+)" "ZNF281_(+)"
## [266] "ZNF316_(+)" "ZNF426_(+)" "ZNF441_(+)" "ZNF449_(+)" "ZNF467_(+)"
## [271] "ZNF471_(+)" "ZNF483_(+)" "ZNF486_(+)" "ZNF502_(+)" "ZNF513_(+)"
## [276] "ZNF560_(+)" "ZNF571_(+)" "ZNF597_(+)" "ZNF668_(+)" "ZNF713_(+)"
## [281] "ZNF717_(+)" "ZNF721_(+)" "ZNF740_(+)" "ZNF816_(+)" "ZNF841_(+)"
## [286] "ZSCAN31_(+)" "ZXDC_(+)"
##
## $RSV_infected_A549
## [1] "ARNT_(+)" "BCLAF1_(+)" "BHLHE40_(+)" "CDX1_(+)" "CLOCK_(+)"
## [6] "CREB5_(+)" "ELF2_(+)" "ELK3_(+)" "EP300_(+)" "ETV1_(+)"
## [11] "ETV3_(+)" "FOSL2_(+)" "GMEB1_(+)" "HEY1_(+)" "HOXB6_(+)"
## [16] "IRX2_(+)" "KLF7_(+)" "MAX_(+)" "MNX1_(+)" "MXD3_(+)"
## [21] "MYCN_(+)" "NFATC2_(+)" "NFKB1_(+)" "NFYA_(+)" "NFYC_(+)"
## [26] "NKX3-1_(+)" "NR2F1_(+)" "PKNOX1_(+)" "POLR3A_(+)" "POLR3G_(+)"
## [31] "PPARD_(+)" "PRDM16_(+)" "REL_(+)" "RREB1_(+)" "SMAD1_(+)"
## [36] "SNAI3_(+)" "SP5_(+)" "TCF7_(+)" "ZFX_(+)" "ZNF208_(+)"
## [41] "ZNF264_(+)" "ZNF267_(+)" "ZNF426_(+)" "ZNF429_(+)" "ZNF502_(+)"
## [46] "ZNF560_(+)" "ZNF773_(+)"
##
## $SARSCoV2_infected_A549
## [1] "ALX1_(+)" "ALX3_(+)" "ALX4_(+)" "ARNT_(+)" "ARNT2_(+)"
## [6] "ARNTL2_(+)" "ATF2_(+)" "ATF4_(+)" "ATF6_(+)" "BACH1_(+)"
## [11] "BBX_(+)" "BCL11A_(+)" "BCL11B_(+)" "BCLAF1_(+)" "BHLHA15_(+)"
## [16] "BHLHE40_(+)" "BRF1_(+)" "CDX2_(+)" "CELF6_(+)" "CLOCK_(+)"
## [21] "CPEB1_(+)" "CTCFL_(+)" "DLX3_(+)" "DLX4_(+)" "DLX5_(+)"
## [26] "DRGX_(+)" "E2F1_(+)" "E2F2_(+)" "E2F4_(+)" "E2F5_(+)"
## [31] "EHF_(+)" "ELF1_(+)" "ELK1_(+)" "ELK3_(+)" "ELK4_(+)"
## [36] "EN1_(+)" "ERF_(+)" "ETS1_(+)" "ETS2_(+)" "ETV1_(+)"
## [41] "ETV3_(+)" "FEV_(+)" "FLI1_(+)" "FOSL2_(+)" "FOXA3_(+)"
## [46] "FOXD2_(+)" "FOXD3_(+)" "FOXF2_(+)" "FOXP4_(+)" "FOXQ1_(+)"
## [51] "GABPA_(+)" "GABPB1_(+)" "GATA5_(+)" "GCM1_(+)" "GLI1_(+)"
## [56] "GLIS2_(+)" "GRHL3_(+)" "GRHPR_(+)" "GSX2_(+)" "GTF2B_(+)"
## [61] "GTF2IRD1_(+)" "GTF3A_(+)" "HES5_(+)" "HES6_(+)" "HEY1_(+)"
## [66] "HIC1_(+)" "HLX_(+)" "HNF1A_(+)" "HNF1B_(+)" "HOXA1_(+)"
## [71] "HOXA6_(+)" "HOXA7_(+)" "HOXB13_(+)" "HOXB2_(+)" "HOXB5_(+)"
## [76] "HOXB6_(+)" "HOXB8_(+)" "HOXC10_(+)" "HOXC11_(+)" "HOXC6_(+)"
## [81] "HOXD3_(+)" "HOXD4_(+)" "HOXD9_(+)" "HSF4_(+)" "HTATIP2_(+)"
## [86] "IKZF2_(+)" "ING3_(+)" "IRF2_(+)" "IRF6_(+)" "IRF9_(+)"
## [91] "IRX1_(+)" "IRX2_(+)" "IRX4_(+)" "JUND_(+)" "KLF10_(+)"
## [96] "KLF6_(+)" "KLF7_(+)" "KLF8_(+)" "LBX2_(+)" "LHX2_(+)"
## [101] "LMX1B_(+)" "MAF_(+)" "MAFA_(+)" "MAX_(+)" "MAZ_(+)"
## [106] "MECOM_(+)" "MEOX1_(+)" "MESP1_(+)" "MNX1_(+)" "MXD1_(+)"
## [111] "MXD3_(+)" "MXI1_(+)" "MYCN_(+)" "MYOD1_(+)" "MYPOP_(+)"
## [116] "MZF1_(+)" "NEUROD1_(+)" "NFATC4_(+)" "NFE2L1_(+)" "NFIA_(+)"
## [121] "NFIB_(+)" "NFKB1_(+)" "NFYA_(+)" "NFYC_(+)" "NHLH2_(+)"
## [126] "NKX2-1_(+)" "NKX2-5_(+)" "NKX3-1_(+)" "NR1H3_(+)" "NR1H4_(+)"
## [131] "NR2E1_(+)" "NR2F1_(+)" "NR2F6_(+)" "NR6A1_(+)" "ONECUT2_(+)"
## [136] "OTP_(+)" "OTX1_(+)" "PATZ1_(+)" "PAX7_(+)" "PAX9_(+)"
## [141] "PHF8_(+)" "PKNOX1_(+)" "POLE4_(+)" "POLR2A_(+)" "POLR3A_(+)"
## [146] "POLR3G_(+)" "POU3F4_(+)" "PRDM1_(+)" "PRDM15_(+)" "PRRX2_(+)"
## [151] "PSMD12_(+)" "RAB18_(+)" "RB1_(+)" "RFX1_(+)" "RFX2_(+)"
## [156] "RREB1_(+)" "RUNX2_(+)" "RXRG_(+)" "SATB1_(+)" "SHOX2_(+)"
## [161] "SIN3A_(+)" "SIX5_(+)" "SMAD1_(+)" "SMARCA5_(+)" "SMARCC2_(+)"
## [166] "SNAI1_(+)" "SNAI2_(+)" "SNAI3_(+)" "SOX5_(+)" "SP3_(+)"
## [171] "SP5_(+)" "SPDEF_(+)" "STAT1_(+)" "STAT5B_(+)" "TAL2_(+)"
## [176] "TCF3_(+)" "TCF7L1_(+)" "TCF7L2_(+)" "TEAD2_(+)" "TFAP2A_(+)"
## [181] "TFAP2C_(+)" "TFDP2_(+)" "THRA_(+)" "TLX1_(+)" "TLX2_(+)"
## [186] "TRIM28_(+)" "USF1_(+)" "USF2_(+)" "VAX1_(+)" "VSX2_(+)"
## [191] "ZFHX3_(+)" "ZFP64_(+)" "ZFX_(+)" "ZIC2_(+)" "ZNF134_(+)"
## [196] "ZNF143_(+)" "ZNF219_(+)" "ZNF267_(+)" "ZNF281_(+)" "ZNF319_(+)"
## [201] "ZNF35_(+)" "ZNF354C_(+)" "ZNF426_(+)" "ZNF429_(+)" "ZNF471_(+)"
## [206] "ZNF483_(+)" "ZNF486_(+)" "ZNF502_(+)" "ZNF513_(+)" "ZNF524_(+)"
## [211] "ZNF554_(+)" "ZNF560_(+)" "ZNF583_(+)" "ZNF597_(+)" "ZNF672_(+)"
## [216] "ZNF720_(+)" "ZNF729_(+)" "ZNF730_(+)" "ZNF75A_(+)" "ZNF768_(+)"
## [221] "ZNF773_(+)" "ZNF787_(+)"
##
## $SARSCoV2_infected_A549_hACE2
## [1] "ATF3_(+)" "CEBPD_(+)" "DLX2_(+)" "EGR3_(+)" "EMX1_(+)"
## [6] "ETV6_(+)" "ETV7_(+)" "FOXK1_(+)" "HIVEP2_(+)" "HOXB6_(+)"
## [11] "IRF1_(+)" "IRF5_(+)" "IRF8_(+)" "IRF9_(+)" "KLF13_(+)"
## [16] "MEF2B_(+)" "NEUROD2_(+)" "NFE2L2_(+)" "NKX3-1_(+)" "RXRG_(+)"
## [21] "SMARCA5_(+)" "SOX2_(+)" "SOX21_(+)" "STAT1_(+)" "STAT2_(+)"
## [26] "TP53_(+)" "TP73_(+)" "XBP1_(+)" "ZNF768_(+)"
##
## $SARSCoV2_infected_A549_hACE2_pt
## [1] "AIRE_(+)" "AKR1A1_(+)" "ALX4_(+)" "ARID5B_(+)" "ARNT_(+)"
## [6] "ATF3_(+)" "BCLAF1_(+)" "BHLHA15_(+)" "BHLHE40_(+)" "BRF2_(+)"
## [11] "CDX2_(+)" "CEBPA_(+)" "CEBPD_(+)" "CLOCK_(+)" "CTCFL_(+)"
## [16] "DLX2_(+)" "DLX3_(+)" "DLX4_(+)" "DMRT1_(+)" "DMRTA1_(+)"
## [21] "E2F1_(+)" "E2F2_(+)" "E2F7_(+)" "E2F8_(+)" "EBF1_(+)"
## [26] "EGR1_(+)" "EHF_(+)" "ELF1_(+)" "ELF4_(+)" "ELK3_(+)"
## [31] "ELK4_(+)" "EMX1_(+)" "ENO1_(+)" "ERF_(+)" "ETS1_(+)"
## [36] "ETS2_(+)" "ETV6_(+)" "ETV7_(+)" "FOSB_(+)" "FOSL2_(+)"
## [41] "FOXB1_(+)" "FOXD3_(+)" "FOXK1_(+)" "FOXK2_(+)" "FOXM1_(+)"
## [46] "FOXN1_(+)" "FOXO1_(+)" "FOXP1_(+)" "GATA5_(+)" "GLI1_(+)"
## [51] "GRHPR_(+)" "GSC2_(+)" "GTF2B_(+)" "HES6_(+)" "HEY1_(+)"
## [56] "HIVEP2_(+)" "HLX_(+)" "HNF4G_(+)" "HOXA2_(+)" "HOXA6_(+)"
## [61] "HOXA9_(+)" "HOXB1_(+)" "HOXB6_(+)" "HOXC11_(+)" "HOXC5_(+)"
## [66] "HOXC9_(+)" "HOXD4_(+)" "HSF1_(+)" "IKZF2_(+)" "IRF1_(+)"
## [71] "IRF2_(+)" "IRF5_(+)" "IRF6_(+)" "IRF7_(+)" "IRF8_(+)"
## [76] "IRF9_(+)" "IRX5_(+)" "JUND_(+)" "KDM4D_(+)" "KLF13_(+)"
## [81] "KLF5_(+)" "KLF6_(+)" "KLF7_(+)" "LHX1_(+)" "LHX2_(+)"
## [86] "LMX1B_(+)" "MAFB_(+)" "MAX_(+)" "MAZ_(+)" "MLXIP_(+)"
## [91] "MLXIPL_(+)" "MNX1_(+)" "MXD3_(+)" "MYCN_(+)" "MYEF2_(+)"
## [96] "MZF1_(+)" "NEUROD2_(+)" "NFATC2_(+)" "NFE2L1_(+)" "NFE2L2_(+)"
## [101] "NFKB1_(+)" "NKX2-2_(+)" "NKX3-1_(+)" "NPDC1_(+)" "NR1D1_(+)"
## [106] "NR1H3_(+)" "NR1H4_(+)" "NR2F1_(+)" "NR2F6_(+)" "NR3C2_(+)"
## [111] "NR4A1_(+)" "ONECUT2_(+)" "PBX3_(+)" "PDX1_(+)" "PHF21A_(+)"
## [116] "POLE4_(+)" "POLR3A_(+)" "POLR3G_(+)" "POU2F3_(+)" "POU3F1_(+)"
## [121] "POU3F4_(+)" "PPARD_(+)" "PRDM16_(+)" "PRRX1_(+)" "PRRX2_(+)"
## [126] "REL_(+)" "RFX1_(+)" "RFXAP_(+)" "RUNX2_(+)" "RXRG_(+)"
## [131] "SATB1_(+)" "SETDB1_(+)" "SHOX2_(+)" "SIX2_(+)" "SIX5_(+)"
## [136] "SMARCA5_(+)" "SMARCC2_(+)" "SNAI3_(+)" "SOX2_(+)" "SOX21_(+)"
## [141] "SOX9_(+)" "SP6_(+)" "SREBF1_(+)" "STAT1_(+)" "STAT2_(+)"
## [146] "STAT5B_(+)" "TAF1_(+)" "TBX20_(+)" "TBX3_(+)" "TCF12_(+)"
## [151] "TEAD2_(+)" "TET1_(+)" "TFAP2A_(+)" "TFAP2C_(+)" "TGIF1_(+)"
## [156] "TP73_(+)" "TRIM28_(+)" "TWIST1_(+)" "USF1_(+)" "VAX1_(+)"
## [161] "VEZF1_(+)" "XBP1_(+)" "ZBED1_(+)" "ZBTB41_(+)" "ZFHX3_(+)"
## [166] "ZFX_(+)" "ZNF239_(+)" "ZNF274_(+)" "ZNF276_(+)" "ZNF460_(+)"
## [171] "ZNF502_(+)" "ZNF524_(+)" "ZNF579_(+)" "ZNF583_(+)" "ZNF652_(+)"
## [176] "ZNF668_(+)" "ZNF672_(+)" "ZNF701_(+)" "ZNF721_(+)" "ZNF730_(+)"
## [181] "ZNF768_(+)" "ZNF773_(+)" "ZNF787_(+)" "ZXDC_(+)"
##
## $SARSCoV2_infected_Calu3
## [1] "AKR1A1_(+)" "ALX4_(+)" "AR_(+)"
## [4] "ARID3C_(+)" "ARID5B_(+)" "ARNT_(+)"
## [7] "ARNT2_(+)" "ARNTL_(+)" "ARNTL2_(+)"
## [10] "ARX_(+)" "ASCL1_(+)" "ASCL2_(+)"
## [13] "ATF3_(+)" "ATF4_(+)" "ATF6_(+)"
## [16] "ATF6B_(+)" "BACH2_(+)" "BATF_(+)"
## [19] "BBX_(+)" "BCL11A_(+)" "BCL3_(+)"
## [22] "BDP1_(+)" "BHLHE40_(+)" "BHLHE41_(+)"
## [25] "BNC2_(+)" "BORCS8-MEF2B_(+)" "BRCA1_(+)"
## [28] "BRF2_(+)" "CCNT2_(+)" "CDX2_(+)"
## [31] "CEBPD_(+)" "CEBPG_(+)" "CIC_(+)"
## [34] "CPEB1_(+)" "CREB3L1_(+)" "CREB5_(+)"
## [37] "CREBL2_(+)" "CTCF_(+)" "CUX1_(+)"
## [40] "DBP_(+)" "DDIT3_(+)" "DLX2_(+)"
## [43] "DLX3_(+)" "DLX4_(+)" "DMRT1_(+)"
## [46] "DRGX_(+)" "DUX4_(+)" "DUXA_(+)"
## [49] "E2F1_(+)" "E2F2_(+)" "E2F8_(+)"
## [52] "E4F1_(+)" "EGR2_(+)" "EGR3_(+)"
## [55] "EHF_(+)" "ELF1_(+)" "ELF4_(+)"
## [58] "ELK1_(+)" "ELK3_(+)" "ELK4_(+)"
## [61] "EMX1_(+)" "ENO1_(+)" "EOMES_(+)"
## [64] "EP300_(+)" "ESRRA_(+)" "ESX1_(+)"
## [67] "ETS1_(+)" "ETS2_(+)" "ETV3_(+)"
## [70] "ETV4_(+)" "ETV6_(+)" "ETV7_(+)"
## [73] "EVX2_(+)" "FBXL19_(+)" "FEV_(+)"
## [76] "FOSB_(+)" "FOSL1_(+)" "FOSL2_(+)"
## [79] "FOXA2_(+)" "FOXA3_(+)" "FOXC1_(+)"
## [82] "FOXC2_(+)" "FOXD2_(+)" "FOXD3_(+)"
## [85] "FOXD4L6_(+)" "FOXF1_(+)" "FOXF2_(+)"
## [88] "FOXG1_(+)" "FOXJ2_(+)" "FOXJ3_(+)"
## [91] "FOXK1_(+)" "FOXL1_(+)" "FOXM1_(+)"
## [94] "FOXN1_(+)" "FOXO1_(+)" "FOXO3_(+)"
## [97] "FOXO4_(+)" "GABPB1_(+)" "GATA2_(+)"
## [100] "GATA3_(+)" "GATA5_(+)" "GATA6_(+)"
## [103] "GBX1_(+)" "GLI1_(+)" "GLIS2_(+)"
## [106] "GMEB1_(+)" "GMEB2_(+)" "GRHL1_(+)"
## [109] "GRHL2_(+)" "GRHPR_(+)" "GSC_(+)"
## [112] "GSC2_(+)" "GTF2B_(+)" "GTF2IRD1_(+)"
## [115] "GTF3A_(+)" "HAND1_(+)" "HDAC2_(+)"
## [118] "HES1_(+)" "HES5_(+)" "HES6_(+)"
## [121] "HESX1_(+)" "HEY2_(+)" "HINFP_(+)"
## [124] "HIVEP1_(+)" "HIVEP2_(+)" "HLF_(+)"
## [127] "HLX_(+)" "HMGB1_(+)" "HNF1A_(+)"
## [130] "HOXA2_(+)" "HOXA6_(+)" "HOXA9_(+)"
## [133] "HOXB4_(+)" "HOXB6_(+)" "HOXB7_(+)"
## [136] "HOXB8_(+)" "HOXB9_(+)" "HOXC10_(+)"
## [139] "HOXC11_(+)" "HOXC13_(+)" "HOXC4_(+)"
## [142] "HOXC6_(+)" "HOXC8_(+)" "HOXD1_(+)"
## [145] "HOXD10_(+)" "HOXD4_(+)" "HOXD8_(+)"
## [148] "HOXD9_(+)" "HSF1_(+)" "HSF2_(+)"
## [151] "IKZF2_(+)" "ING3_(+)" "IRF1_(+)"
## [154] "IRF2_(+)" "IRF4_(+)" "IRF5_(+)"
## [157] "IRF6_(+)" "IRF7_(+)" "IRF8_(+)"
## [160] "IRF9_(+)" "IRX2_(+)" "ISL2_(+)"
## [163] "JUN_(+)" "JUND_(+)" "KDM4D_(+)"
## [166] "KDM5A_(+)" "KLF10_(+)" "KLF11_(+)"
## [169] "KLF12_(+)" "KLF13_(+)" "KLF14_(+)"
## [172] "KLF15_(+)" "KLF16_(+)" "KLF2_(+)"
## [175] "KLF5_(+)" "KLF6_(+)" "KLF7_(+)"
## [178] "KLF8_(+)" "KLF9_(+)" "LBX1_(+)"
## [181] "LHX1_(+)" "LHX4_(+)" "LHX5_(+)"
## [184] "LMX1B_(+)" "MAFA_(+)" "MAFB_(+)"
## [187] "MAFK_(+)" "MAX_(+)" "MAZ_(+)"
## [190] "MECOM_(+)" "MEF2C_(+)" "MEIS1_(+)"
## [193] "MEOX2_(+)" "MESP1_(+)" "MIOS_(+)"
## [196] "MITF_(+)" "MLXIP_(+)" "MLXIPL_(+)"
## [199] "MXI1_(+)" "MYBL1_(+)" "MYCN_(+)"
## [202] "MYEF2_(+)" "MYOD1_(+)" "MYPOP_(+)"
## [205] "MZF1_(+)" "NEUROD1_(+)" "NFATC2_(+)"
## [208] "NFE2_(+)" "NFE2L1_(+)" "NFE2L2_(+)"
## [211] "NFE2L3_(+)" "NFIC_(+)" "NFKB1_(+)"
## [214] "NFYA_(+)" "NFYC_(+)" "NHLH1_(+)"
## [217] "NHLH2_(+)" "NKX3-1_(+)" "NKX6-2_(+)"
## [220] "NPAS2_(+)" "NPDC1_(+)" "NR0B1_(+)"
## [223] "NR1H2_(+)" "NR1H3_(+)" "NR1H4_(+)"
## [226] "NR1I2_(+)" "NR2E1_(+)" "NR2F1_(+)"
## [229] "NR2F6_(+)" "NR4A1_(+)" "NR5A2_(+)"
## [232] "NRF1_(+)" "OLIG2_(+)" "OLIG3_(+)"
## [235] "ONECUT2_(+)" "OTP_(+)" "OVOL1_(+)"
## [238] "PAX7_(+)" "PAX8_(+)" "PAX9_(+)"
## [241] "PBX1_(+)" "PDX1_(+)" "PHF2_(+)"
## [244] "PHF21A_(+)" "PHF8_(+)" "PHOX2A_(+)"
## [247] "PKNOX1_(+)" "POLE3_(+)" "POLR2A_(+)"
## [250] "POLR3A_(+)" "POU2F1_(+)" "POU4F1_(+)"
## [253] "POU5F1_(+)" "POU5F1B_(+)" "POU6F1_(+)"
## [256] "PPARA_(+)" "PPARD_(+)" "PRDM1_(+)"
## [259] "PRDM16_(+)" "PRRX1_(+)" "PRRX2_(+)"
## [262] "RAD21_(+)" "RARA_(+)" "RARG_(+)"
## [265] "RBBP5_(+)" "REL_(+)" "RFX7_(+)"
## [268] "RREB1_(+)" "RUNX2_(+)" "SATB1_(+)"
## [271] "SETDB1_(+)" "SHOX2_(+)" "SIN3A_(+)"
## [274] "SIX1_(+)" "SIX2_(+)" "SIX5_(+)"
## [277] "SMAD1_(+)" "SMARCA5_(+)" "SMARCC2_(+)"
## [280] "SMC3_(+)" "SNAI3_(+)" "SNAPC4_(+)"
## [283] "SOX11_(+)" "SOX13_(+)" "SOX15_(+)"
## [286] "SOX2_(+)" "SOX21_(+)" "SOX4_(+)"
## [289] "SOX9_(+)" "SP1_(+)" "SP2_(+)"
## [292] "SP6_(+)" "SP7_(+)" "SP9_(+)"
## [295] "SPDEF_(+)" "SREBF2_(+)" "SRF_(+)"
## [298] "STAT1_(+)" "STAT2_(+)" "STAT4_(+)"
## [301] "STAT5B_(+)" "STAU2_(+)" "TAF7_(+)"
## [304] "TAL1_(+)" "TBP_(+)" "TBPL2_(+)"
## [307] "TBX15_(+)" "TBX18_(+)" "TBX20_(+)"
## [310] "TBX6_(+)" "TCF3_(+)" "TCF4_(+)"
## [313] "TCF7_(+)" "TCF7L1_(+)" "TEAD4_(+)"
## [316] "TET1_(+)" "TFAP2A_(+)" "TFAP2C_(+)"
## [319] "TFDP1_(+)" "TFDP2_(+)" "TFE3_(+)"
## [322] "TFEB_(+)" "TGIF2_(+)" "THAP1_(+)"
## [325] "TLX1_(+)" "TLX2_(+)" "TP73_(+)"
## [328] "TRIM28_(+)" "UBP1_(+)" "USF1_(+)"
## [331] "VAX1_(+)" "VAX2_(+)" "VDR_(+)"
## [334] "VEZF1_(+)" "VSX1_(+)" "VSX2_(+)"
## [337] "WT1_(+)" "YBX1_(+)" "YY2_(+)"
## [340] "ZBTB33_(+)" "ZEB1_(+)" "ZFHX2_(+)"
## [343] "ZFY_(+)" "ZNF134_(+)" "ZNF143_(+)"
## [346] "ZNF160_(+)" "ZNF208_(+)" "ZNF219_(+)"
## [349] "ZNF25_(+)" "ZNF264_(+)" "ZNF267_(+)"
## [352] "ZNF276_(+)" "ZNF316_(+)" "ZNF354C_(+)"
## [355] "ZNF384_(+)" "ZNF429_(+)" "ZNF441_(+)"
## [358] "ZNF471_(+)" "ZNF483_(+)" "ZNF486_(+)"
## [361] "ZNF513_(+)" "ZNF524_(+)" "ZNF550_(+)"
## [364] "ZNF554_(+)" "ZNF560_(+)" "ZNF606_(+)"
## [367] "ZNF607_(+)" "ZNF668_(+)" "ZNF672_(+)"
## [370] "ZNF713_(+)" "ZNF717_(+)" "ZNF721_(+)"
## [373] "ZNF761_(+)" "ZNF768_(+)" "ZNF787_(+)"
## [376] "ZNF8_(+)" "ZSCAN31_(+)" "ZXDC_(+)"
##
## $SARSCoV2_infected_NHBE
## [1] "AHCTF1_(+)" "AIRE_(+)" "ALX1_(+)" "ARID5B_(+)" "ARNT_(+)"
## [6] "ATF1_(+)" "BATF_(+)" "BBX_(+)" "BCL3_(+)" "BCL6B_(+)"
## [11] "CEBPB_(+)" "CEBPG_(+)" "CKMT1B_(+)" "CREB1_(+)" "CREB3_(+)"
## [16] "CREBL2_(+)" "DLX4_(+)" "DMRT1_(+)" "DMRTA1_(+)" "DUXA_(+)"
## [21] "EBF1_(+)" "EGR2_(+)" "ERF_(+)" "ESRRA_(+)" "ETV1_(+)"
## [26] "ETV6_(+)" "ETV7_(+)" "FOSL1_(+)" "FOXA2_(+)" "FOXF2_(+)"
## [31] "FOXK2_(+)" "FOXP3_(+)" "FOXQ1_(+)" "GATA2_(+)" "GATA3_(+)"
## [36] "HDAC2_(+)" "HES5_(+)" "HLF_(+)" "HNF1A_(+)" "HNF1B_(+)"
## [41] "HOXA2_(+)" "HOXA5_(+)" "HOXA7_(+)" "HOXA9_(+)" "HOXB5_(+)"
## [46] "HOXB6_(+)" "HOXB7_(+)" "HOXB8_(+)" "HOXB9_(+)" "HOXC10_(+)"
## [51] "HOXC4_(+)" "HOXC5_(+)" "HOXC6_(+)" "HOXC8_(+)" "HSF2_(+)"
## [56] "ING3_(+)" "IRF1_(+)" "IRF3_(+)" "IRF4_(+)" "IRF5_(+)"
## [61] "IRF8_(+)" "IRF9_(+)" "IRX2_(+)" "ISL2_(+)" "JUN_(+)"
## [66] "KLF10_(+)" "KLF5_(+)" "LHX1_(+)" "LHX4_(+)" "LHX8_(+)"
## [71] "LHX9_(+)" "MAFF_(+)" "MEOX2_(+)" "MIOS_(+)" "MLXIP_(+)"
## [76] "MSX1_(+)" "MSX2_(+)" "MXD4_(+)" "MYEF2_(+)" "NFIL3_(+)"
## [81] "NFYB_(+)" "NHLH1_(+)" "NKX2-1_(+)" "NKX3-1_(+)" "NR1I2_(+)"
## [86] "NR4A1_(+)" "NRF1_(+)" "ONECUT2_(+)" "OVOL2_(+)" "PAX2_(+)"
## [91] "PAX7_(+)" "PBX1_(+)" "PDX1_(+)" "PKNOX1_(+)" "POU2F3_(+)"
## [96] "PRDM16_(+)" "RARG_(+)" "RBBP5_(+)" "RELA_(+)" "RFX5_(+)"
## [101] "SIX2_(+)" "SMC3_(+)" "SOX13_(+)" "SOX2_(+)" "SP1_(+)"
## [106] "SP2_(+)" "SP4_(+)" "SP7_(+)" "SREBF2_(+)" "STAT1_(+)"
## [111] "STAT2_(+)" "TAF7_(+)" "TAGLN2_(+)" "TAL1_(+)" "TBP_(+)"
## [116] "TBPL1_(+)" "TBPL2_(+)" "TBX15_(+)" "TBX3_(+)" "TCF7_(+)"
## [121] "TET1_(+)" "TFAP2A_(+)" "TFE3_(+)" "TFEB_(+)" "TLX3_(+)"
## [126] "TWIST1_(+)" "UBTF_(+)" "UQCRB_(+)" "VSX1_(+)" "WT1_(+)"
## [131] "ZEB1_(+)" "ZFHX2_(+)" "ZFY_(+)" "ZNF160_(+)" "ZNF471_(+)"
## [136] "ZNF502_(+)" "ZNF560_(+)" "ZNF580_(+)" "ZNF768_(+)" "ZNF773_(+)"
## [141] "ZXDC_(+)"
##
## $human_IFNB_treated_NHBE
## [1] "ATF3_(+)" "BACH2_(+)" "ELF3_(+)" "ETV6_(+)" "ETV7_(+)"
## [6] "FOXA1_(+)" "FOXK1_(+)" "GATA2_(+)" "GCM1_(+)" "GTF2B_(+)"
## [11] "HIVEP2_(+)" "HLX_(+)" "HOXD11_(+)" "IRF1_(+)" "IRF2_(+)"
## [16] "IRF7_(+)" "IRF8_(+)" "IRF9_(+)" "NFIL3_(+)" "NFIX_(+)"
## [21] "NKX3-1_(+)" "POU5F1B_(+)" "PRDM16_(+)" "RARA_(+)" "RELA_(+)"
## [26] "RXRB_(+)" "SOX11_(+)" "SOX9_(+)" "STAT2_(+)" "TFEB_(+)"
## [31] "VEZF1_(+)"# Load and tidy data: adjusted pvalues, format
paste0("using pvalues from test : ", adj_pvals_mwu.fn)## [1] "using pvalues from test : ../regulons-analysis/out/celllines/pvalues-klm-2t-adjusted.csv"pvalues <- as.data.frame(read_csv(adj_pvals_mwu.fn, col_names = T))
if(params$celllines==1){
names(pvalues) <- str_replace_all(names(pvalues), "-", "")
names(pvalues) <- str_replace(names(pvalues), "w_vector_expressing_", "")
names(pvalues) <- str_replace(names(pvalues), "1hr_Ruxolitinib_pretreatment", "pt")
}
rownames(pvalues) <- tfs
# retrieve regs/tfs in DA
selectedTFsUp <- selectTFs(tfs_per_exp_list = tfs_da_up)
# selectedTFsDown <- selectTFs(tfs_per_exp_list = tfs_da_up, test = "downregulated")
selectedTFs <- selectedTFsUp
# subset pvalues for selectedTFs
pvalues <- pvalues[rownames(pvalues) %in% selectedTFs,]
pvalues <- t(as.matrix(pvalues))Basic heatmap
Regulons that passed Mann Whitney and
Kolmogrov Tests for Differential Activation with and
FDR Adjusted Pval of <= 0.2. Only Mann Whitney Adjusted
pvalue is shown.
# heatmap colors
col_fun = colorRamp2(c(0, 0.2, 1), c("red", "white", "steelblue1")) # red is cool, blue is bad pval
Heatmap(pvalues, name = "Adj pvalue", col = col_fun,
column_title = "Per Experiment Downregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"), column_names_gp = gpar(fontsize = 2))
TF family info
# heatmap colors
col_fun = colorRamp2(c(0, 0.2, 1), c("red", "white", "steelblue1"))
## Tf families annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(pvalues))## [1] "Warning! 43 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in TF family order
pvalues <- pvalues[,match(tf_fam_dbd$TF_Name,colnames(pvalues))]
## Celllines and infection annotation
if(params$celllines==1){
# reorder experiments according to clustering
order <- c(11,4,6,7,8,9,1,3,5,10,2)
pvalues <- pvalues[order,]
meta <- meta[match(rownames(pvalues),meta$source),] # order as in matrix
## cell lines
cell.lines <- meta$cellline
cols.cells <- RColorBrewer::brewer.pal(n=3, name = "Set1")
names(cols.cells) <- unique(cell.lines)
## treatment
treatment <- str_replace(
str_remove(str_remove(str_extract(meta$source, "^.+ed_"), "_infected_"), "_treated_"), "human_","h")
cols.trtm <- RColorBrewer::brewer.pal(n=length(unique(treatment)), name = "Set3")
names(cols.trtm) <- unique(treatment)
}
## Plots
# TF family annotation
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, col = list(TF_family = cols.tfs),
annotation_name_side = "left", annotation_name_gp = gpar(fontsize = 9),
annotation_legend_param = list(TF_family = list(direction = "horizontal", nrow = 3)))
# experiment cell lines and treatment annotation
h2 <- rowAnnotation(Cell_line = cell.lines, Treatment = treatment, annotation_name_gp = gpar(fontsize = 9),
col = list(Cell_line = cols.cells, Treatment = cols.trtm),
annotation_legend_param = list(Cell_line = list(direction = "horizontal", nrow = 1),
Treatment = list(direction = "horizontal", nrow = 1)))
## regs_heatmap_DA_[UP|DOWN]_tffam.png
htmp <- Heatmap(pvalues, name = "Adj pvalue", cluster_columns = FALSE, col = col_fun,
column_title = "Per Experiment Downregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 2),
row_names_gp = gpar(fontsize = 10),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## regs_heatmap_DA_[UP|DOWN]_tffam_tfclust.png
htmp <- Heatmap(pvalues, name = "Adj pvalue", cluster_columns = TRUE, col = col_fun,
column_title = "Per Experiment Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 2),
row_names_gp = gpar(fontsize = 10),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Heatmap auc_mtx
## Load and tidy data to plot: pvalues adjusted of regulons in DA [UP|DOWN]
# Load metadata to name samples as experiments
meta2 <- as.data.frame(read_csv(params$metadata.fn, col_names = T))
if(params$celllines == 1){
meta2 <- meta2 %>% dplyr::select(ID,source)
meta2$source <- str_remove(meta2$source, "_treated")
meta2$source <- str_remove(meta2$source, "_infected")
meta2$source <- str_replace_all(meta2$source, "-", "")
meta2$source <- str_remove(meta2$source, "w_vector_expressing_")
meta2$source <- str_replace(meta2$source, "1hr_Ruxolitinib_pretreatment", "pt")
}
# Load and tidy data: adjusted pvalues, format
auc_mtx.hm <- auc_mtx
colnames(auc_mtx.hm) <- tfs
# rearrange meta as in auc_mtx
meta2 <- meta2[match(rownames(auc_mtx.hm),meta2$ID),]
meta2$source_num <- paste0(meta2$source,"-",1:74)
rownames(auc_mtx.hm) <- meta2$source_num
paste0("using auc_mtx")## [1] "using auc_mtx"Basic heatmap
Regulons that passed Mann Whitney and
Kolmogrov Tests for Differential Activation with and
FDR Adjusted Pval of <= 0.2.
# # heatmap colors
# max.auc = max(apply(auc_mtx, 1, max))
# median.auc = summary(apply(auc_mtx, 1, sum)/length(colnames(auc_mtx)))[3]
# min.auc = min(apply(auc_mtx, 1, min))
# col_fun = colorRamp2(c(max.auc, median.auc, min.auc), c("red", "white", "steelblue1")) # red is cool, blue is bad pval
# # retrieve regs/tfs in DA
# selectedTFs <- selectTFs(tfs_per_exp_list = tfs_da_up[6:10])
# # subset pvalues for selectedTFs
# auc_mtx.hm <- as.matrix(auc_mtx.hm[,colnames(auc_mtx.hm) %in% selectedTFs])
# Heatmap(auc_mtx.hm, name = "AUC", col = col_fun,
# column_title = "Per Experiment Upregulated Regulons",
# column_title_gp = gpar(fontsize = 10, fontface = "bold"),
# column_names_gp = gpar(fontsize = 2), row_names_gp = gpar(fontsize = 5))Heatmaps showing AUC values for the subset of regulons that were Differentially Activated - upregulated in SARS-CoV-2 infection experiments per cell line.
NHBE
## NHBE
# retrieve regs/tfs in DA
selectedTFs <- selectTFs(tfs_per_exp_list = tfs_da_up[10])
# subset pvalues for selectedTFs
auc_mtx.hm.nhbe <- as.matrix(auc_mtx.hm[str_detect(rownames(auc_mtx.hm),"NHBE"), ## show nhbe experiments
colnames(auc_mtx.hm) %in% selectedTFs]) ## up regs sc2 nhbe
max.auc = max(apply(auc_mtx.hm.nhbe, 1, max))
median.auc = summary(apply(auc_mtx.hm.nhbe, 1, sum)/length(colnames(auc_mtx.hm.nhbe)))[3]
min.auc = min(apply(auc_mtx.hm.nhbe, 1, min))
col_fun = colorRamp2(c(max.auc, median.auc, min.auc), c("red", "white", "steelblue1")) # red is cool, blue is bad pval
hm <- Heatmap(auc_mtx.hm.nhbe, name = "AUC", col = col_fun,
column_title = "NHBE SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 10, fontface = "bold"), column_names_rot = 45,
column_names_gp = gpar(fontsize = 5), row_names_gp = gpar(fontsize = 10))
hm
## save to png
# png(paste0(params$outdir,"heatmap_upreg_nhbe.png"), width = 900, height = 600)
# hm
# dev.off()## Tf families annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(auc_mtx.hm.nhbe))## [1] "Warning! 10 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in TF family order
auc_mtx.hm.nhbe <- auc_mtx.hm.nhbe[,match(tf_fam_dbd$TF_Name,colnames(auc_mtx.hm.nhbe))]
## Celllines and infection annotation
if(params$celllines==1){
# reorder experiments according to clustering
## nhbe
order <- c(7,8,5,6,22,21,23,24,19,20,17,18,16,11,10,9,1,2,4,3,13,12,15,14)
auc_mtx.hm.cl <- auc_mtx.hm.nhbe[order,]
meta2.sub <- meta2[match(rownames(auc_mtx.hm.cl),meta2$source_num),] # order as in matrix
## sc2 infected
sc2_infected <- rep("No",length(rownames(auc_mtx.hm.cl)))
sc2_infected[str_detect(meta2.sub$source,"SARSCoV2")] <- "Yes"
cols.sc2 <- RColorBrewer::brewer.pal(n=2, name = "Set3")
names(cols.sc2) <- unique(sc2_infected)
cols.sc2 <- cols.sc2[1:2]
}
## Plots
# TF family annotation
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, col = list(TF_family = cols.tfs),
annotation_name_side = "left", annotation_name_gp = gpar(fontsize = 9),
annotation_legend_param = list(TF_family = list(direction = "horizontal", nrow = 3)))
# experiment cell lines and treatment annotation
h2 <- rowAnnotation(SARSCov2 = sc2_infected, annotation_name_gp = gpar(fontsize = 9),
col = list(SARSCov2 = cols.sc2),
annotation_legend_param = list(SARSCov2 = list(direction = "horizontal", nrow = 1)))
## regs_heatmap_DA_[UP|DOWN]_tffam.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = FALSE, col = col_fun,
column_title = "NHBE SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## regs_heatmap_DA_[UP|DOWN]_tffam_tfclust.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = TRUE, col = col_fun,
column_title = "NHBE SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
# png(paste0(params$outdir,"heatmap_upreg_nhbe_tfannot.png"), width = 900, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()Calu3
## Calu3
selectedTFs <- selectTFs(tfs_per_exp_list = tfs_da_up[9])
auc_mtx.hm.calu3 <- as.matrix(auc_mtx.hm[str_detect(rownames(auc_mtx.hm),"Calu3"), ## show nhbe experiments
colnames(auc_mtx.hm) %in% selectedTFs]) ## up regs sc2 nhbe
## readjust colors
max.auc = max(apply(auc_mtx.hm.calu3, 1, max))
median.auc = summary(apply(auc_mtx.hm.calu3, 1, sum)/length(colnames(auc_mtx.hm.calu3)))[3]
min.auc = min(apply(auc_mtx.hm.calu3, 1, min))
col_fun = colorRamp2(c(max.auc, median.auc, min.auc), c("red", "white", "steelblue1")) # red is cool, blue is bad pval
## Plot
hm <- Heatmap(auc_mtx.hm.calu3, name = "AUC", col = col_fun,
column_title = "Calu3 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 10, fontface = "bold"), column_names_rot = 45,
column_names_gp = gpar(fontsize = 3), row_names_gp = gpar(fontsize = 8))
hm
## save to png
# png(paste0(params$outdir,"heatmap_upreg_calu3.png"), width = 900, height = 600)
# hm
# dev.off()## Tf families annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(auc_mtx.hm.calu3))## [1] "Warning! 21 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in TF family order
auc_mtx.hm.calu3 <- auc_mtx.hm.calu3[,match(tf_fam_dbd$TF_Name,colnames(auc_mtx.hm.calu3))]
## Celllines and infection annotation
if(params$celllines==1){
# reorder experiments according to clustering
## nhbe
order <- c(4:6,1,3,2)
auc_mtx.hm.cl <- auc_mtx.hm.calu3[order,]
meta2.sub <- meta2[match(rownames(auc_mtx.hm.cl),meta2$source_num),] # order as in matrix
## sc2 infected
sc2_infected <- rep("No",length(rownames(auc_mtx.hm.cl)))
sc2_infected[str_detect(meta2.sub$source,"SARSCoV2")] <- "Yes"
cols.sc2 <- RColorBrewer::brewer.pal(n=2, name = "Set3")
names(cols.sc2) <- unique(sc2_infected)
cols.sc2 <- cols.sc2[1:2]
}
## Plots
# TF family annotation
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, col = list(TF_family = cols.tfs),
annotation_name_side = "left", annotation_name_gp = gpar(fontsize = 9),
annotation_legend_param = list(TF_family = list(direction = "horizontal", nrow = 3)))
# experiment cell lines and treatment annotation
h2 <- rowAnnotation(SARSCov2 = sc2_infected, annotation_name_gp = gpar(fontsize = 9),
col = list(SARSCov2 = cols.sc2),
annotation_legend_param = list(SARSCov2 = list(direction = "horizontal", nrow = 1)))
## regs_heatmap_DA_[UP|DOWN]_tffam.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = FALSE, col = col_fun,
column_title = "Calu3 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## regs_heatmap_DA_[UP|DOWN]_tffam_tfclust.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = TRUE, col = col_fun,
column_title = "Calu3 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
# png(paste0(params$outdir,"heatmap_upreg_calu3_tfannot.png"), width = 900, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()A549
## A549
selectedTFs <- selectTFs(tfs_per_exp_list = tfs_da_up[6:8])
auc_mtx.hm.a549 <- as.matrix(auc_mtx.hm[str_detect(rownames(auc_mtx.hm),"A549"), ## show nhbe experiments
colnames(auc_mtx.hm) %in% selectedTFs]) ## up regs sc2 nhbe
max.auc = max(apply(auc_mtx.hm.a549, 1, max))
median.auc = summary(apply(auc_mtx.hm.a549, 1, sum)/length(colnames(auc_mtx.hm.a549)))[3]
min.auc = min(apply(auc_mtx.hm.a549, 1, min))
col_fun = colorRamp2(c(max.auc, median.auc, min.auc), c("red", "white", "steelblue1"))
hm <- Heatmap(auc_mtx.hm.a549, name = "AUC", col = col_fun,
column_title = "A549 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 10, fontface = "bold"), column_names_rot = 45,
column_names_gp = gpar(fontsize = 4), row_names_gp = gpar(fontsize = 8))
hm
## save to png
# png(paste0(params$outdir,"heatmap_upreg_a549.png"), width = 900, height = 600)
# hm
# dev.off()TF family info
## Tf families annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(auc_mtx.hm.a549))## [1] "Warning! 11 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in TF family order
auc_mtx.hm.a549 <- auc_mtx.hm.a549[,match(tf_fam_dbd$TF_Name,colnames(auc_mtx.hm.a549))]
## Celllines and infection annotation
if(params$celllines==1){
# reorder experiments according to clustering
## nhbe
## calu3
## a549
order <- c(41,40,39,42:44,36:38,29,27,28,1,3,2,34,35,33,4,5,25,26,10,
9,30,32,31,6:8,18,15,14,17,13,11,12,20,21,19,23,24,22,16)
auc_mtx.hm.cl <- auc_mtx.hm.a549[order,]
meta2.sub <- meta2[match(rownames(auc_mtx.hm.cl),meta2$source_num),] # order as in matrix
## sc2 infected
sc2_infected <- rep("No",length(rownames(auc_mtx.hm.cl)))
sc2_infected[str_detect(meta2.sub$source,"SARSCoV2")] <- "Yes"
cols.sc2 <- RColorBrewer::brewer.pal(n=2, name = "Set3")
names(cols.sc2) <- unique(sc2_infected)
cols.sc2 <- cols.sc2[1:2]
}
## Plots
# TF family annotation
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, col = list(TF_family = cols.tfs),
annotation_name_side = "left", annotation_name_gp = gpar(fontsize = 9),
annotation_legend_param = list(TF_family = list(direction = "horizontal", nrow = 3)))
# experiment cell lines and treatment annotation
h2 <- rowAnnotation(SARSCov2 = sc2_infected, annotation_name_gp = gpar(fontsize = 9),
col = list(SARSCov2 = cols.sc2),
annotation_legend_param = list(SARSCov2 = list(direction = "horizontal", nrow = 1)))
## regs_heatmap_DA_[UP|DOWN]_tffam.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = FALSE, col = col_fun,
column_title = "A549 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## regs_heatmap_DA_[UP|DOWN]_tffam_tfclust.png
htmp <- Heatmap(auc_mtx.hm.cl, name = "AUC", cluster_columns = TRUE, col = col_fun,
column_title = "A549 SARS-CoV-2 infected Upregulated Regulons",
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 6),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
png(paste0(params$outdir,"heatmap_upreg_a549_tfannot.png"), width = 900, height = 600)
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
dev.off()## png
## 2GO Enrichment Analysis
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
# Load and tidy data: regulons/tfs that were in DA + enriched
# Store all genes from all upregulated regulons per experiment to search for enriched terms
regs_genes_per_exp <- regsGenesPerExp(tfs_per_exp_list = tfs_da_up, tfs_targets = tfs_targets, entrezids = T)Number of genes in all regulons per experiment
# number of genes in all regulons per experiment
t(as.data.frame(lapply(regs_genes_per_exp, length)))## [,1]
## HPIV3_infected_A549 15746
## IAV_infected_A549 16147
## IAV_infected_NHBE 16638
## IAVdNS1_infected_NHBE 16629
## RSV_infected_A549 13287
## SARSCoV2_infected_A549 15659
## SARSCoV2_infected_A549_hACE2 14156
## SARSCoV2_infected_A549_hACE2_pt 16128
## SARSCoV2_infected_Calu3 16777
## SARSCoV2_infected_NHBE 16459
## human_IFNB_treated_NHBE 14161GO:BP analysis
# Perform GO Enrichment Analysis on regulons per experiment case
# BP Biological Process, CC Cellular Component, MF Molecular Functions
ego_perexp_da <- list()
i <- 1
for (regs in regs_genes_per_exp) {
ego <- enrichGO(gene = regs, universe = genes_universe, # change
OrgDb = org.Hs.eg.db, ont = "BP", pAdjustMethod = "BH", pvalueCutoff = 0.01,
qvalueCutoff = 0.05, readable = TRUE)
ego_perexp_da[i] <- list(ego)
i=i+1
}
names(ego_perexp_da) <- names(regs_genes_per_exp)
# ego_perexp_da <- ego_perexp_da[-which(t(as.data.frame(lapply(ego_perexp_da, is.na))))]
## Generate Dotplots iteratively
plots <- iterDotplot(ego_perexp_da, db = "GO:BP", savepngs = F, outdir = params$outdir, test_name = "upregulated")
# names(plots) <- names(ego_perexp_da)
plots## $HPIV3_infected_A549
##
## $RSV_infected_A549
##
## $SARSCoV2_infected_A549
##
## $SARSCoV2_infected_A549_hACE2
##
## $SARSCoV2_infected_A549_hACE2_pt
##
## $human_IFNB_treated_NHBE
# if(params$celllines==1){
# # display plots together
# p <- ggarrange(plots[[1]],plots[[2]],plots[[3]], ncol = 3, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated TFs",face = "bold", size = 14))
# p <- ggarrange(plots[[4]],plots[[5]], plots[[11]], ncol = 3, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated TFs",face = "bold", size = 14))
# p <- ggarrange(plots[[6]],plots[[7]],ncol = 2, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated TFs",face = "bold", size = 14))
# p <- ggarrange(plots[[8]],plots[[10]],ncol = 2, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated TFs",face = "bold", size = 14))
# }KEGG Enrichment Analysis
# Perform KEGG Pathway over-representation Analysis on regulons per experiment case
kk_perexp_da <- list()
i <- 1
for (regs in regs_genes_per_exp) {
kk <- enrichKEGG(gene = regs, organism = 'hsa', pvalueCutoff = 0.05,
pAdjustMethod = "BH", universe = genes_universe)
kk_perexp_da[i] <- list(kk)
i=i+1
}
names(kk_perexp_da) <- names(regs_genes_per_exp)
## Generate Dotplots iteratively
plots <- iterDotplot(kk_perexp_da, db = "KEGG", savepngs = F, outdir = params$outdir, test_name = "upregulated")
# names(plots) <- names(ego_perexp_da)
plotsEnrichment Analysis per Regulon
GO:BO
Perform Enrichment Analysis runs for all regulons (tfs and target genes)
# Perform GO Enrichment Analysis on individual regulons
# BP Biological Process, CC Cellular Component, MF Molecular Functions
ego_per_regulon <- list()
for (tf in names(tfs_targets)) {
regulon <- unique(c(str_remove(tf,"_\\(\\+\\)"), tfs_targets[[tf]]))
ego <- enrichGO(gene = regulon, universe = genes_universe.df$SYMBOL, keyType = "SYMBOL",
OrgDb = org.Hs.eg.db, ont = "BP", pAdjustMethod = "BH",
pvalueCutoff = 0.01, qvalueCutoff = 0.05, readable = TRUE)
ego_per_regulon[[tf]] <- ego
}Number of regulons for which at least one enriched term was found:
For each experiment, take GOEA of each regulon and make a cloudplot of the terms:
# For each experiment, take GOEA of each regulon and make a cloudplot of the terms:
plots_per_exp <- list()
for (exp in names(tfs_per_exp_list)) {
# Store ego enriched terms per regulon
terms_perexp <- c()
for (tf in names(tfs_per_exp_list[[exp]])) {
# take top n enriched terms per regulon of experiment
enriched_terms <- as.data.frame(ego_per_regulon[[tf]])[,2]
enriched_terms <- str_replace_all(enriched_terms," ","_")
if(length(enriched_terms) > n){
enriched_terms <- enriched_terms[1:n]
}
terms_perexp <- c(terms_perexp, enriched_terms)
wordcloud(terms_perexp, max.words = 1,
colors = RColorBrewer::brewer.pal(1,name = "Paired"))
}
plots_per_exp[[exp]] <- wordcloud(terms_perexp, max.words = 1,
colors = RColorBrewer::brewer.pal(1,name = "Paired"))
}KEGG
Plots: DA+enrichment Regulon Selection
Upset Plot
Upregulated Regulons
library(tidyverse)
library(ComplexHeatmap)
library(RColorBrewer)
# Load and tidy data: regulons/tfs that were in DA + enriched
# tfs_da_enrich
## Upset Plot
# subset for experiments that had intersections with other experiments, also plot only intersections >=2
#m <- make_comb_mat(lfc[names(lfc) %in% has.intersections], mode = "intersect")
m <- make_comb_mat(tfs_da_enrich_up, mode = "distinct")
#m <- m[comb_degree(m) >= 2]
ss = set_size(m)
cs = comb_size(m)
ht = UpSet(m,
set_order = order(ss),
comb_order = order(comb_degree(m), -cs),
top_annotation = HeatmapAnnotation(
"Regulons Intersection" = anno_barplot(cs,
ylim = c(0, max(cs)*1.1),
border = FALSE,
gp = gpar(fill = "black"),
height = unit(4, "cm")
),
annotation_name_side = "left",
annotation_name_rot = 90),
left_annotation = rowAnnotation(
"# Regulons" = anno_barplot(-ss,
baseline = 0,
border = FALSE,
gp = gpar(fill = "black"),
width = unit(4, "cm")
),
set_name = anno_text(set_name(m),
location = 0.5,
just = "center",
width = max_text_width(set_name(m)) + unit(4, "mm"))
),
right_annotation = NULL,
show_row_names = FALSE,
pt_size = unit(5, "mm"),
lwd = 3,
comb_col = RColorBrewer::brewer.pal(n = max(comb_degree(m)), name = "Set1")[comb_degree(m)]
)
ht = draw(ht)
od = column_order(ht)
decorate_annotation("Regulons Intersection", {
grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
default.units = "native", just = c("left", "bottom"),
gp = gpar(fontsize = 11, col = "#404040"), rot = 45)
})
# ## save to png
# png(paste0(params$outdir,"upset_upregulated_enrich.png"), width = 1000, height = 600)
# ht = draw(ht)
# od = column_order(ht)
# decorate_annotation("Regulons Intersection", {
# grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
# default.units = "native", just = c("left", "bottom"),
# gp = gpar(fontsize = 11, col = "#404040"), rot = 45)
# })
# dev.off()Pairwise comparison of shared regulons, distinctly shared regulons are shown in the next table:
tableCommonDistinct <- distinctlySharedRegs(tfs_per_exp_list = tfs_da_enrich_up)
View(tableCommonDistinct)
# save table
# write_csv(tableCommonDistinct,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_up-enrich.csv"), col_names = T)
## subset for SARS-CoV-2 infected experiments
tabledown.sc2 <- tableCommonDistinct %>%
dplyr::select(6:10) %>% dplyr::slice(6:10)
View(tabledown.sc2)
# save table
# write_csv(tabledown.sc2,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_down-enrich_subSC2.csv"), col_names = T)Regulons that are only present in SARS-CoV-2 infection samples:
getDistinct(tfs_per_exp_list = tfs_da_enrich_up, indexInterestGroup = 6:10)## [1] "AHCTF1" "ARID5B" "NR1I2" "ZNF502"Downregulated Regulons
library(tidyverse)
library(ComplexHeatmap)
library(RColorBrewer)
# Load and tidy data: regulons/tfs that were in DA + enriched
# tfs_da_enrich
## Upset Plot
# subset for experiments that had intersections with other experiments, also plot only intersections >=2
#m <- make_comb_mat(lfc[names(lfc) %in% has.intersections], mode = "intersect")
m <- make_comb_mat(tfs_da_enrich_down, mode = "distinct")
#m <- m[comb_degree(m) >= 2]
ss = set_size(m)
cs = comb_size(m)
ht = UpSet(m,
set_order = order(ss),
comb_order = order(comb_degree(m), -cs),
top_annotation = HeatmapAnnotation(
"Regulons Intersection" = anno_barplot(cs,
ylim = c(0, max(cs)*1.1),
border = FALSE,
gp = gpar(fill = "black"),
height = unit(4, "cm")
),
annotation_name_side = "left",
annotation_name_rot = 90),
left_annotation = rowAnnotation(
"# Regulons" = anno_barplot(-ss,
baseline = 0,
border = FALSE,
gp = gpar(fill = "black"),
width = unit(4, "cm")
),
set_name = anno_text(set_name(m),
location = 0.5,
just = "center",
width = max_text_width(set_name(m)) + unit(4, "mm"))
),
right_annotation = NULL,
show_row_names = FALSE,
pt_size = unit(5, "mm"),
lwd = 3,
comb_col = RColorBrewer::brewer.pal(n = max(comb_degree(m)), name = "Set1")[comb_degree(m)]
)
ht = draw(ht)
od = column_order(ht)
decorate_annotation("Regulons Intersection", {
grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
default.units = "native", just = c("left", "bottom"),
gp = gpar(fontsize = 11, col = "#404040"), rot = 45)
})
# ## save to png
# png(paste0(params$outdir,"upset_downregulated_enrich.png"), width = 1000, height = 600)
# ht = draw(ht)
# od = column_order(ht)
# decorate_annotation("Regulons Intersection", {
# grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
# default.units = "native", just = c("left", "bottom"),
# gp = gpar(fontsize = 11, col = "#404040"), rot = 45)
# })
# dev.off()Pairwise comparison of shared regulons, distinctly shared regulons are shown in the next table:
tabledown <- distinctlySharedRegs(tfs_per_exp_list = tfs_da_enrich_down)
tabledown# # save table
# write_csv(tabledown,
# file = paste0(params$outdir,"tableDistinctlySharedRegulons_down-enrich.csv"), col_names = T)Regulons that are only present in SARS-CoV-2 infection samples:
getDistinct(tfs_per_exp_list = tfs_da_enrich_down, indexInterestGroup = 6:10)## [1] "HES5" "ZNF471" "ZNF560"Heatmap
library(tidyverse)
library(ComplexHeatmap)
library(circlize)
## Load and tidy data
rss <- as.data.frame(read_csv(paste0(params$inputdir,params$RSS.fn), col_names = T))
rss <- column_to_rownames(rss, var="...1")
rownames(rss) <- str_remove(rownames(rss),"_\\(\\+\\)")
colnames(rss) <- str_remove(colnames(rss), "[YN]$")
if(params$celllines==1){
colnames(rss) <- str_replace_all(colnames(rss), "-", "")
colnames(rss) <- str_replace(colnames(rss), "w_vector_expressing_", "")
colnames(rss) <- str_replace(colnames(rss), "1hr_Ruxolitinib_pretreatment", "pt")
}
rss <- t(as.matrix(rss))
rss <- rss[,-(which(is.na(apply(rss, 2, sum))))] ## remove tfs with nas
# Load and tidy data: regulons/tfs that were in DA + enriched
## Subset for tfs that were in DA and were enriched in SARSCoV2 experiments
selectedTFsUpLFC <- selectTFs(tfs_per_exp_list = tfs_da_enrich_up[6:10])
rssUp <- rss[,match(selectedTFsUpLFC, colnames(rss))]
## down
selectedTFsDownLFC <- selectTFs(tfs_per_exp_list = tfs_da_enrich_down[6:10])
rssDown <- rss[,match(selectedTFsDownLFC, colnames(rss))]Basic heatmap
## Plot
Heatmap(rssUp, name = "RSS",
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 10, fontface = "bold"), column_names_rot = 45,
column_names_gp = gpar(fontsize = 5), row_names_gp = gpar(fontsize = 10))
Annotating TFs by families
## TF Family annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(rssUp))## [1] "Warning! 12 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in tf fam order
rssUp <- rssUp[,match(tf_fam_dbd$TF_Name,colnames(rssUp))]
## highlight immune related tfs
immune.tfs <- rep("No",length(selectedTFsUpLFC))
immune.tfs[tf_fam_dbd$Family_Name %in%
c("Ets","GATA","IRF","Rel","RFX","SMAD","STAT","ARID/BRIGHT","HSF","p53")] <- "Yes"
cols.tfsi <- c("#c51b7d","#7fbc41") # colors vector
names(cols.tfsi) <- c("Yes","No")
# reorder experiments in matrix by clustering order
# if(params$sub_cases_rss_plots == 1){
# exp.sorted <- rownames(rss)[order(rownames(rss))]
# rss <- rss[match(exp.sorted,rownames(rss)),]
# if(params$celllines == 1){
# order <- c(2,5,4,10,7,8,9,1,6,11,3)
# rss <- rss[order,]
# }
# }
order <- c(3,14,13,1,12,5,11,4,7,10,8,15,2,6,9)
rssUp <- rssUp[order,]
## Celllines and infection annotation
if(params$celllines==1){
## sc2 infected
sc2_infected <- rep("No",length(rownames(rssUp)))
sc2_infected[str_detect(rownames(rssUp),"SARSCoV2")] <- "Yes"
cols.sc2 <- RColorBrewer::brewer.pal(n=2, name = "Set3")
names(cols.sc2) <- unique(sc2_infected)
cols.sc2 <- cols.sc2[1:2]
}
# heatmap colors
# col_fun = colorRamp2(c(0, 0.2, 0.23, 0.4), c("blue", "white","indianred1", "red3"))
## plot RSS metric colors
max.rss = max(apply(rssUp, 1, max))
firstqrt.rss = mean(apply(rssUp, 1, summary)[2,])
thirdqrt.rss = mean(apply(rssUp, 1, summary)[5,])
mean.rss = mean(apply(rssUp, 1, summary)[3,])
#min.rss = min(apply(rssUp, 1, min))
col_fun = colorRamp2(c(0.4, thirdqrt.rss, firstqrt.rss, 0), c("red3", "indianred1", "white", "blue"))
## Plot
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, Immune_TF = immune.tfs,
col = list(TF_family = cols.tfs, Immune_TF = cols.tfsi),
annotation_name_side = "left",
annotation_legend_param =
list(TF_family = list(direction = "horizontal", nrow = 3),
Immune_TF = list(direction = "horizontal", nrow = 1)))
# h2 <- rowAnnotation(Cell_line = cell.lines, Treatment = treatment,
# col = list(Cell_line = cols.cells, Treatment = cols.trtm),
# annotation_legend_param = list(Cell_line = list(direction = "horizontal", nrow = 1),
# Treatment = list(direction = "horizontal", nrow = 1)))
h2 <- rowAnnotation(SARSCov2 = sc2_infected, annotation_name_gp = gpar(fontsize = 9),
col = list(SARSCov2 = cols.sc2),
annotation_legend_param = list(SARSCov2 = list(direction = "horizontal", nrow = 1)))
htmp <- Heatmap(rssUp, name = "RSS", cluster_columns = FALSE, col = col_fun,
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 5),
row_names_gp = gpar(fontsize = 9),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
# png(paste0(params$outdir,"heatmap_upreg_enrich_tfannot_notfclust.png"), width = 900, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()
# cluster TFs
htmp <- Heatmap(rssUp, name = "RSS", cluster_columns = TRUE, col = col_fun,
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 5),
row_names_gp = gpar(fontsize = 9),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
# png(paste0(params$outdir,"heatmap_upreg_enrich_tfannot_tfclust.png"), width = 900, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()Heatmap only showing infected or treated samples:
## removing controls
rssUp.nc <- rssUp[str_detect(rownames(rssUp),"Mock")==F,]
## sc2 infected bar
sc2_infected <- rep("No",length(rownames(rssUp.nc)))
sc2_infected[str_detect(rownames(rssUp.nc),"SARSCoV2")] <- "Yes"
cols.sc2 <- RColorBrewer::brewer.pal(n=2, name = "Set3")
names(cols.sc2) <- unique(sc2_infected)
cols.sc2 <- cols.sc2[1:2]
## Plot
h2 <- rowAnnotation(SARSCov2 = sc2_infected, annotation_name_gp = gpar(fontsize = 9),
col = list(SARSCov2 = cols.sc2),
annotation_legend_param = list(SARSCov2 = list(direction = "horizontal", nrow = 1)))
# cluster TFs
htmp <- Heatmap(rssUp.nc, name = "RSS", cluster_columns = TRUE, col = col_fun,
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 3),
row_names_gp = gpar(fontsize = 8),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
# ## save to png
# png(paste0(params$outdir,"heatmap_upreg_enrich_tfannot_tfclust_subInf.png"), width = 900, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()removing Calu3 DA + enriched regulons
selectedTFsUpLFC <- selectTFs(tfs_per_exp_list = tfs_da_enrich_up[c(6:8,10)])
rssUp.nc2 <- rssUp.nc[,match(selectedTFsUpLFC, colnames(rssUp.nc))]
## TF Family annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(rssUp.nc2))## [1] "Warning! 1 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in tf fam order
rssUp.nc2 <- rssUp.nc2[,match(tf_fam_dbd$TF_Name,colnames(rssUp.nc2))]
## highlight immune related tfs
immune.tfs <- rep("No",length(selectedTFsUpLFC))
immune.tfs[tf_fam_dbd$Family_Name %in%
c("Ets","GATA","IRF","Rel","RFX","SMAD","STAT","ARID/BRIGHT","HSF","p53")] <- "Yes"
cols.tfsi <- c("#c51b7d","#7fbc41") # colors vector
names(cols.tfsi) <- c("Yes","No")
## Plot
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, Immune_TF = immune.tfs,
col = list(TF_family = cols.tfs, Immune_TF = cols.tfsi),
annotation_name_side = "left",
annotation_legend_param =
list(TF_family = list(direction = "horizontal", nrow = 3),
Immune_TF = list(direction = "horizontal", nrow = 1)))
# cluster TFs
htmp <- Heatmap(rssUp.nc2, name = "RSS", cluster_columns = TRUE, col = col_fun,
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_rot = 45, column_names_gp = gpar(fontsize = 10),
row_names_gp = gpar(fontsize = 10),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
## save to png
# png(paste0(params$outdir,"heatmap_upreg_enrich_tfannot_tfclust_subInf_nosc2calu3regs.png"), width = 900, height = 400)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()Only show Calu3 SARS-CoV-2 regulons
selectedTFsUpLFC <- selectTFs(tfs_per_exp_list = tfs_da_enrich_up[9])
rssUp.nc3 <- rssUp.nc[,match(selectedTFsUpLFC, colnames(rssUp.nc))]
## TF Family annotation
tf_fam_dbd <- family_annot(tfs_interest = colnames(rssUp.nc3))## [1] "Warning! 11 TFs were not found in the database. Annotating them as UNKNOWN"cols.tfs <- rainbow(n = length(unique(tf_fam_dbd$Family_Name))) # colors vector
names(cols.tfs) <- unique(tf_fam_dbd$Family_Name)
# rearrange matrix in tf fam order
rssUp.nc3 <- rssUp.nc3[,match(tf_fam_dbd$TF_Name,colnames(rssUp.nc3))]
## highlight immune related tfs
immune.tfs <- rep("No",length(selectedTFsUpLFC))
immune.tfs[tf_fam_dbd$Family_Name %in%
c("Ets","GATA","IRF","Rel","RFX","SMAD","STAT","ARID/BRIGHT","HSF","p53")] <- "Yes"
cols.tfsi <- c("#c51b7d","#7fbc41") # colors vector
names(cols.tfsi) <- c("Yes","No")
# change experiment names
rownames(rssUp.nc3) <- str_remove(rownames(rssUp.nc3), "_infected")
rownames(rssUp.nc3) <- str_remove(rownames(rssUp.nc3), "_treated")
## Plot
h1 <- HeatmapAnnotation(TF_family = tf_fam_dbd$Family_Name, Immune_TF = immune.tfs,
col = list(TF_family = cols.tfs, Immune_TF = cols.tfsi),
annotation_name_side = "left",
annotation_legend_param =
list(TF_family = list(direction = "horizontal", nrow = 3),
Immune_TF = list(direction = "horizontal", nrow = 1)))
# cluster TFs
htmp <- Heatmap(rssUp.nc3, name = "RSS", cluster_columns = TRUE, col = col_fun,
column_title = "Per Experiment Most Enriched Upregulated Regulons",
column_title_gp = gpar(fontsize = 15, fontface = "bold"),
column_names_gp = gpar(fontsize = 6),
row_names_gp = gpar(fontsize = 5),
bottom_annotation = h1, right_annotation = h2,
heatmap_legend_param = list(direction = "horizontal"))
draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
# ## save to png
# png(paste0(params$outdir,"heatmap_upreg_enrich_tfannot_tfclust_subInf_sc2calu3regs.png"), width = 1000, height = 600)
# draw(htmp, merge_legend = TRUE, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()GO Term Enrichment Analysis
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
# Load and tidy data: regulons/tfs that were in DA + enriched
# Store all genes from all upregulated regulons per experiment to search for enriched terms
regs_genes_per_exp <- regsGenesPerExp(tfs_per_exp_list = tfs_da_enrich_up, tfs_targets = tfs_targets, entrezids = T)Number of genes in all regulons per experiment
# number of genes in all regulons per experiment
t(as.data.frame(lapply(regs_genes_per_exp, length)))## [,1]
## HPIV3_infected_A549 549
## IAV_infected_A549 145
## IAV_infected_NHBE 11748
## IAVdNS1_infected_NHBE 15165
## RSV_infected_A549 0
## SARSCoV2_infected_A549 2731
## SARSCoV2_infected_A549_hACE2 12022
## SARSCoV2_infected_A549_hACE2_pt 2645
## SARSCoV2_infected_Calu3 16499
## SARSCoV2_infected_NHBE 1878
## human_IFNB_treated_NHBE 12309# Perform GO Enrichment Analysis on regulons per experiment case
# BP Biological Process, CC Cellular Component, MF Molecular Functions
ego_perexp_da <- list()
i <- 1
for (regs in regs_genes_per_exp) {
ego <- enrichGO(gene = regs, universe = genes_universe, # change
OrgDb = org.Hs.eg.db, ont = "BP", pAdjustMethod = "BH", pvalueCutoff = 0.01,
qvalueCutoff = 0.05, readable = TRUE)
ego_perexp_da[i] <- list(ego)
i=i+1
}
names(ego_perexp_da) <- names(regs_genes_per_exp)
## Generate Dotplots iteratively
plots <- iterDotplot(ego_perexp_da, db = "GO:BP", savepngs = F, outdir = params$outdir, test_name = "upregulated-rss")
plots## $IAV_infected_A549
##
## $IAV_infected_NHBE
##
## $IAVdNS1_infected_NHBE
##
## $SARSCoV2_infected_A549
##
## $SARSCoV2_infected_A549_hACE2
##
## $SARSCoV2_infected_A549_hACE2_pt
##
## $SARSCoV2_infected_NHBE
##
## $human_IFNB_treated_NHBE
# if(params$celllines==1){
# # display plots together
# p <- ggarrange(plots$IAVdNS1_infected_NHBE,plots$human_IFNB_treated_NHBE, ncol = 2, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated+Enriched Regulons",
# face = "bold", size = 14))
# p <- ggarrange(plots$SARSCoV2_infected_A549_hACE2,plots$SARSCoV2_infected_A549_hACE2_pt,
# plots$SARSCoV2_infected_Calu3,ncol = 3, nrow = 1)
# annotate_figure(p, top = text_grob("GO:BP Enriched Terms in Upregulated+Enriched Regulons",
# face = "bold", size = 14))
# }KEGG Enrichment Analysis
# Perform KEGG Pathway over-representation Analysis on regulons per experiment case
kk_perexp_da <- list()
i <- 1
for (regs in regs_genes_per_exp) {
kk <- enrichKEGG(gene = regs, organism = 'hsa', pvalueCutoff = 0.05,
pAdjustMethod = "BH", universe = genes_universe)
kk_perexp_da[i] <- list(kk)
i=i+1
}
names(kk_perexp_da) <- names(regs_genes_per_exp)
## Generate Dotplots iteratively
plots <- iterDotplot(kk_perexp_da, db = "KEGG", savepngs = F, outdir = params$outdir, test_name = "upregulated-rss")
plotsDisease Ontology Enrichment Analysis
DOSE supports enrichment analysis of Disease Ontology (DO) (Schriml et al. 2011), Network of Cancer Gene (A. et al. 2016) and Disease Gene Network (DisGeNET) (Janet et al. 2015).
library(DOSE)
# Perform Disease Enrichment Analysis qith only the tfs (no target genes) per experiment case
edo_perexp_da <- list()
i <- 1
for (regs in regs_genes_per_exp) {
edo <- enrichDO(gene = regs, pvalueCutoff = 0.1, ont = "DO",
pAdjustMethod = "BH", universe = genes_universe)
edo_perexp_da[i] <- list(edo)
i=i+1
}
names(edo_perexp_da) <- names(regs_genes_per_exp)
## Generate Dotplots iteratively
plots <- iterDotplot(edo_perexp_da, db = "DO", savepngs = T, outdir = params$outdir, test_name = "upregulated-rss")
plots## $HPIV3_infected_A549
##
## $IAV_infected_NHBE
##
## $IAVdNS1_infected_NHBE
##
## $SARSCoV2_infected_A549_hACE2
##
## $SARSCoV2_infected_NHBE
##
## $human_IFNB_treated_NHBE
References
[1] https://www.cienciadedatos.net/documentos/19b_comparaciones_multiples_correccion_p-value_fdr
[2] https://yulab-smu.top/biomedical-knowledge-mining-book/clusterprofiler-go.html
sessionInfo()## R version 4.2.2 Patched (2022-11-10 r83330)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=es_MX.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=es_MX.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=es_MX.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] wordcloud_2.6 RColorBrewer_1.1-3 DOSE_3.24.2
## [4] pathview_1.38.0 clusterProfiler_4.6.0 enrichplot_1.18.1
## [7] ggpubr_0.5.0 org.Hs.eg.db_3.16.0 AnnotationDbi_1.60.0
## [10] IRanges_2.32.0 S4Vectors_0.36.0 Biobase_2.58.0
## [13] BiocGenerics_0.44.0 circlize_0.4.15 ComplexHeatmap_2.14.0
## [16] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [19] purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
## [22] tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] shadowtext_0.1.2 readxl_1.4.1 backports_1.4.1
## [4] fastmatch_1.1-3 plyr_1.8.8 igraph_1.3.5
## [7] lazyeval_0.2.2 splines_4.2.2 BiocParallel_1.32.3
## [10] GenomeInfoDb_1.34.3 digest_0.6.30 yulab.utils_0.0.5
## [13] foreach_1.5.2 htmltools_0.5.3 GOSemSim_2.24.0
## [16] viridis_0.6.2 GO.db_3.16.0 fansi_1.0.3
## [19] magrittr_2.0.3 memoise_2.0.1 googlesheets4_1.0.1
## [22] cluster_2.1.4 doParallel_1.0.17 tzdb_0.3.0
## [25] Biostrings_2.66.0 graphlayouts_0.8.4 modelr_0.1.10
## [28] matrixStats_0.63.0 vroom_1.6.0 timechange_0.1.1
## [31] colorspace_2.0-3 blob_1.2.3 rvest_1.0.3
## [34] ggrepel_0.9.2 haven_2.5.1 xfun_0.35
## [37] crayon_1.5.2 RCurl_1.98-1.9 jsonlite_1.8.3
## [40] graph_1.76.0 scatterpie_0.1.8 ape_5.6-2
## [43] iterators_1.0.14 glue_1.6.2 polyclip_1.10-4
## [46] gtable_0.3.1 gargle_1.2.1 zlibbioc_1.44.0
## [49] XVector_0.38.0 GetoptLong_1.0.5 car_3.1-1
## [52] Rgraphviz_2.42.0 shape_1.4.6 abind_1.4-5
## [55] scales_1.2.1 DBI_1.1.3 rstatix_0.7.1
## [58] Rcpp_1.0.9 viridisLite_0.4.1 clue_0.3-63
## [61] tidytree_0.4.1 gridGraphics_0.5-1 bit_4.0.5
## [64] httr_1.4.4 fgsea_1.24.0 ellipsis_0.3.2
## [67] XML_3.99-0.12 pkgconfig_2.0.3 farver_2.1.1
## [70] sass_0.4.4 dbplyr_2.2.1 utf8_1.2.2
## [73] labeling_0.4.2 ggplotify_0.1.0 tidyselect_1.2.0
## [76] rlang_1.0.6 reshape2_1.4.4 munsell_0.5.0
## [79] cellranger_1.1.0 tools_4.2.2 cachem_1.0.6
## [82] downloader_0.4 cli_3.4.1 generics_0.1.3
## [85] RSQLite_2.2.19 gson_0.0.9 broom_1.0.1
## [88] evaluate_0.18 fastmap_1.1.0 yaml_2.3.6
## [91] ggtree_3.6.2 knitr_1.41 bit64_4.0.5
## [94] fs_1.5.2 tidygraph_1.2.2 KEGGREST_1.38.0
## [97] ggraph_2.1.0 nlme_3.1-160 KEGGgraph_1.58.0
## [100] aplot_0.1.9 xml2_1.3.3 compiler_4.2.2
## [103] rstudioapi_0.14 png_0.1-7 ggsignif_0.6.4
## [106] treeio_1.22.0 reprex_2.0.2 tweenr_2.0.2
## [109] bslib_0.4.1 stringi_1.7.8 highr_0.9
## [112] lattice_0.20-45 Matrix_1.5-1 vctrs_0.5.1
## [115] pillar_1.8.1 lifecycle_1.0.3 jquerylib_0.1.4
## [118] GlobalOptions_0.1.2 data.table_1.14.6 cowplot_1.1.1
## [121] bitops_1.0-7 patchwork_1.1.2 qvalue_2.30.0
## [124] R6_2.5.1 gridExtra_2.3 codetools_0.2-18
## [127] MASS_7.3-58 assertthat_0.2.1 rjson_0.2.21
## [130] withr_2.5.0 GenomeInfoDbData_1.2.9 parallel_4.2.2
## [133] hms_1.1.2 ggfun_0.0.9 HDO.db_0.99.1
## [136] rmarkdown_2.18 carData_3.0-5 googledrive_2.0.0
## [139] ggforce_0.4.1 lubridate_1.9.0